mirror of
https://github.com/meta-llama/llama-stack.git
synced 2025-12-06 02:30:58 +00:00
Merge branch 'llamastack:main' into restore_responses_unit_tests
This commit is contained in:
Mike Sager
GitHub
commit
73a114f585
404 changed files with 19023 additions and 1727 deletions
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@ -244,8 +244,14 @@ def make_mcp_server(required_auth_token: str | None = None, tools: dict[str, Cal
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timeout = 2
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start_time = time.time()
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server_url = f"http://localhost:{port}/sse"
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logger.debug(f"Waiting for MCP server thread to start on port {port}")
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# Determine the appropriate host for the server URL based on test environment
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# - For library client and server mode: use localhost (both on same host)
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# - For docker mode: use host.docker.internal (container needs to reach host)
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import os
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mcp_host = os.environ.get("LLAMA_STACK_TEST_MCP_HOST", "localhost")
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server_url = f"http://{mcp_host}:{port}/sse"
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logger.debug(f"Waiting for MCP server thread to start on port {port} (accessible via {mcp_host})")
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while time.time() - start_time < timeout:
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if server_thread.is_alive():
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@ -6,9 +6,7 @@
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from typing import Protocol
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from llama_stack.apis.version import LLAMA_STACK_API_V1
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from llama_stack.providers.datatypes import Api, ProviderSpec, RemoteProviderSpec
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from llama_stack.schema_utils import webmethod
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from llama_stack_api import LLAMA_STACK_API_V1, Api, ProviderSpec, RemoteProviderSpec, webmethod
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def available_providers() -> list[ProviderSpec]:
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@ -14,7 +14,7 @@ from io import BytesIO
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import pytest
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from llama_stack.apis.files import OpenAIFilePurpose
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from llama_stack_api import OpenAIFilePurpose
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class BatchHelper:
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@ -10,8 +10,8 @@ from unittest.mock import patch
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import pytest
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import requests
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from llama_stack.apis.files import OpenAIFilePurpose
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from llama_stack.core.datatypes import User
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from llama_stack_api import OpenAIFilePurpose
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purpose = OpenAIFilePurpose.ASSISTANTS
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@ -16,15 +16,15 @@ from unittest.mock import AsyncMock, patch
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import pytest
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from llama_stack.apis.datatypes import Api
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from llama_stack.apis.inference.inference import (
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from llama_stack.core.library_client import LlamaStackAsLibraryClient
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from llama_stack.core.telemetry.telemetry import MetricEvent
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from llama_stack_api import (
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Api,
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OpenAIAssistantMessageParam,
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OpenAIChatCompletion,
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OpenAIChatCompletionUsage,
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OpenAIChoice,
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)
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from llama_stack.core.library_client import LlamaStackAsLibraryClient
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from llama_stack.core.telemetry.telemetry import MetricEvent
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class OpenAIChatCompletionWithMetrics(OpenAIChatCompletion):
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@ -10,7 +10,8 @@ import uuid
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import pytest
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from llama_stack.apis.post_training import (
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from llama_stack.log import get_logger
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from llama_stack_api import (
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DataConfig,
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DatasetFormat,
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DPOAlignmentConfig,
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@ -18,7 +19,6 @@ from llama_stack.apis.post_training import (
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LoraFinetuningConfig,
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TrainingConfig,
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)
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from llama_stack.log import get_logger
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# Configure logging
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logger = get_logger(name=__name__, category="post_training")
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17
tests/integration/responses/conftest.py
Normal file
17
tests/integration/responses/conftest.py
Normal file
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@ -0,0 +1,17 @@
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# Copyright (c) Meta Platforms, Inc. and affiliates.
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# All rights reserved.
|
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#
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# This source code is licensed under the terms described in the LICENSE file in
|
||||
# the root directory of this source tree.
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import pytest
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from llama_stack.core.library_client import LlamaStackAsLibraryClient
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@pytest.fixture
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def responses_client(compat_client):
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"""Provide a client for responses tests, skipping library client mode."""
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if isinstance(compat_client, LlamaStackAsLibraryClient):
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pytest.skip("Responses API tests are not supported in library client mode")
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return compat_client
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@ -0,0 +1,549 @@
|
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{
|
||||
"test_id": "tests/integration/responses/test_tool_responses.py::test_response_streaming_multi_turn_tool_execution[openai_client-txt=openai/gpt-4o-experiment_analysis_streaming]",
|
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"request": {
|
||||
"method": "POST",
|
||||
"url": "https://api.openai.com/v1/v1/chat/completions",
|
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"headers": {},
|
||||
"body": {
|
||||
"model": "gpt-4o",
|
||||
"messages": [
|
||||
{
|
||||
"role": "user",
|
||||
"content": "I need a complete analysis: First, get the experiment ID for 'chemical_reaction', then get the results for that experiment, and tell me if the yield was above 80%. Return only one tool call per step. Please stream your analysis process."
|
||||
},
|
||||
{
|
||||
"role": "assistant",
|
||||
"content": "",
|
||||
"tool_calls": [
|
||||
{
|
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"index": 0,
|
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"id": "call_Q9Gcxub7UbQsxJWVkiy4FETr",
|
||||
"type": "function",
|
||||
"function": {
|
||||
"name": "get_experiment_id",
|
||||
"arguments": "{\"experiment_name\":\"chemical_reaction\"}"
|
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}
|
||||
}
|
||||
]
|
||||
},
|
||||
{
|
||||
"role": "tool",
|
||||
"tool_call_id": "call_Q9Gcxub7UbQsxJWVkiy4FETr",
|
||||
"content": [
|
||||
{
|
||||
"type": "text",
|
||||
"text": "exp_003"
|
||||
}
|
||||
]
|
||||
}
|
||||
],
|
||||
"stream": true,
|
||||
"stream_options": {
|
||||
"include_usage": true
|
||||
},
|
||||
"tools": [
|
||||
{
|
||||
"type": "function",
|
||||
"function": {
|
||||
"name": "get_user_id",
|
||||
"description": "\n Get the user ID for a given username. This ID is needed for other operations.\n\n :param username: The username to look up\n :return: The user ID for the username\n ",
|
||||
"parameters": {
|
||||
"properties": {
|
||||
"username": {
|
||||
"title": "Username",
|
||||
"type": "string"
|
||||
}
|
||||
},
|
||||
"required": [
|
||||
"username"
|
||||
],
|
||||
"title": "get_user_idArguments",
|
||||
"type": "object"
|
||||
}
|
||||
}
|
||||
},
|
||||
{
|
||||
"type": "function",
|
||||
"function": {
|
||||
"name": "get_user_permissions",
|
||||
"description": "\n Get the permissions for a user ID. Requires a valid user ID from get_user_id.\n\n :param user_id: The user ID to check permissions for\n :return: The permissions for the user\n ",
|
||||
"parameters": {
|
||||
"properties": {
|
||||
"user_id": {
|
||||
"title": "User Id",
|
||||
"type": "string"
|
||||
}
|
||||
},
|
||||
"required": [
|
||||
"user_id"
|
||||
],
|
||||
"title": "get_user_permissionsArguments",
|
||||
"type": "object"
|
||||
}
|
||||
}
|
||||
},
|
||||
{
|
||||
"type": "function",
|
||||
"function": {
|
||||
"name": "check_file_access",
|
||||
"description": "\n Check if a user can access a specific file. Requires a valid user ID.\n\n :param user_id: The user ID to check access for\n :param filename: The filename to check access to\n :return: Whether the user can access the file (yes/no)\n ",
|
||||
"parameters": {
|
||||
"properties": {
|
||||
"user_id": {
|
||||
"title": "User Id",
|
||||
"type": "string"
|
||||
},
|
||||
"filename": {
|
||||
"title": "Filename",
|
||||
"type": "string"
|
||||
}
|
||||
},
|
||||
"required": [
|
||||
"user_id",
|
||||
"filename"
|
||||
],
|
||||
"title": "check_file_accessArguments",
|
||||
"type": "object"
|
||||
}
|
||||
}
|
||||
},
|
||||
{
|
||||
"type": "function",
|
||||
"function": {
|
||||
"name": "get_experiment_id",
|
||||
"description": "\n Get the experiment ID for a given experiment name. This ID is needed to get results.\n\n :param experiment_name: The name of the experiment\n :return: The experiment ID\n ",
|
||||
"parameters": {
|
||||
"properties": {
|
||||
"experiment_name": {
|
||||
"title": "Experiment Name",
|
||||
"type": "string"
|
||||
}
|
||||
},
|
||||
"required": [
|
||||
"experiment_name"
|
||||
],
|
||||
"title": "get_experiment_idArguments",
|
||||
"type": "object"
|
||||
}
|
||||
}
|
||||
},
|
||||
{
|
||||
"type": "function",
|
||||
"function": {
|
||||
"name": "get_experiment_results",
|
||||
"description": "\n Get the results for an experiment ID. Requires a valid experiment ID from get_experiment_id.\n\n :param experiment_id: The experiment ID to get results for\n :return: The experiment results\n ",
|
||||
"parameters": {
|
||||
"properties": {
|
||||
"experiment_id": {
|
||||
"title": "Experiment Id",
|
||||
"type": "string"
|
||||
}
|
||||
},
|
||||
"required": [
|
||||
"experiment_id"
|
||||
],
|
||||
"title": "get_experiment_resultsArguments",
|
||||
"type": "object"
|
||||
}
|
||||
}
|
||||
}
|
||||
]
|
||||
},
|
||||
"endpoint": "/v1/chat/completions",
|
||||
"model": "gpt-4o"
|
||||
},
|
||||
"response": {
|
||||
"body": [
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{
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"__type__": "openai.types.chat.chat_completion_chunk.ChatCompletionChunk",
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"__data__": {
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"choices": [
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"refusal": null,
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"role": "assistant",
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"tool_calls": [
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{
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"index": 0,
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"id": "call_yTMuQEKu7x115q8XvhqelRub",
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"function": {
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"arguments": "",
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||||
"name": "get_experiment_results"
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},
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"type": "function"
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"model": "gpt-4o-2024-08-06",
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}
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],
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"object": "chat.completion.chunk",
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"system_fingerprint": "fp_cbf1785567",
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"obfuscation": "leu"
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"content": "{\"query\": \"Llama 4 Maverick model experts\", \"top_k\": [{\"url\": \"https://console.groq.com/docs/model/meta-llama/llama-4-maverick-17b-128e-instruct\", \"title\": \"Llama 4 Maverick 17B 128E\", \"content\": \"Llama 4 Maverick is Meta's natively multimodal model that enables text and image understanding. With a 17 billion parameter mixture-of-experts architecture (128 experts), this model offers industry-leading performance for multimodal tasks like natural assistant-like chat, image recognition, and coding tasks. Llama 4 Maverick features an auto-regressive language model that uses a mixture-of-experts (MoE) architecture with 17B activated parameters (400B total) and incorporates early fusion for native multimodality. The model uses 128 experts to efficiently handle both text and image inputs while maintaining high performance across chat, knowledge, and code generation tasks, with a knowledge cutoff of August 2024. * For multimodal applications, this model supports up to 5 image inputs create( model =\\\"meta-llama/llama-4-maverick-17b-128e-instruct\\\", messages =[ { \\\"role\\\": \\\"user\\\", \\\"content\\\": \\\"Explain why fast inference is critical for reasoning models\\\" } ] ) print(completion.\", \"score\": 0.9170729, \"raw_content\": null}, {\"url\": \"https://huggingface.co/meta-llama/Llama-4-Maverick-17B-128E\", \"title\": \"meta-llama/Llama-4-Maverick-17B-128E - Hugging Face\", \"content\": \"Model Architecture: The Llama 4 models are auto-regressive language models that use a mixture-of-experts (MoE) architecture and incorporate\", \"score\": 0.8021998, \"raw_content\": null}, {\"url\": \"https://www.ibm.com/new/announcements/meta-llama-4-maverick-and-llama-4-scout-now-available-in-watsonx-ai\", \"title\": \"Meta Llama 4 Maverick and Llama 4 Scout now available in watsonx ...\", \"content\": \"# Meta Llama 4 Maverick and Llama 4 Scout now available in watsonx.ai **IBM is excited to announce the addition of Meta\\u2019s latest generation of open models, Llama 4, to** **watsonx.ai****.** Llama 4 Scout and Llama 4 Maverick, the first mixture of experts (MoE) models released by Meta, provide frontier multimodal performance, high speeds, low cost, and industry leading context length. With the introduction of these latest offerings from Meta, IBM now supports a total of 13 Meta models in the expansive library of \\u00a0foundation models available in watsonx.ai. Trained on 40 trillion tokens of data, Llama 4 Scout offers performance rivalling or exceeding that of models with significantly larger active parameter counts while keeping costs and latency low. ## Llama 4 models on IBM watsonx\", \"score\": 0.78194773, \"raw_content\": null}, {\"url\": \"https://medium.com/@divyanshbhatiajm19/metas-llama-4-family-the-complete-guide-to-scout-maverick-and-behemoth-ai-models-in-2025-21a90c882e8a\", \"title\": \"Meta's Llama 4 Family: The Complete Guide to Scout, Maverick, and ...\", \"content\": \"# Meta\\u2019s Llama 4 Family: The Complete Guide to Scout, Maverick, and Behemoth AI Models in 2025 Feature Llama 4 Scout Llama 4 Maverick Llama 4 Behemoth **Total Parameters** 109B 400B ~2T **Active Parameters** 17B 17B 288B **Expert Count** 16 128 16 **Context Window** 10M tokens 1M tokens Not specified **Hardware Requirements** Single H100 GPU Single H100 DGX host Multiple GPUs **Inference Cost** Not specified $0.19-$0.49 per 1M tokens Not specified **Release Status** Available now Available now In training **Primary Use Cases** Long-context analysis, code processing High-performance multimodal applications Research, STEM reasoning The Llama 4 family represents Meta\\u2019s most significant AI development to date, with each model offering distinct advantages for different use cases:\", \"score\": 0.69672287, \"raw_content\": null}, {\"url\": \"https://www.llama.com/models/llama-4/\", \"title\": \"Unmatched Performance and Efficiency | Llama 4\", \"content\": \"# Llama 4 # Llama 4 Llama 4 Scout Class-leading natively multimodal model that offers superior text and visual intelligence, single H100 GPU efficiency, and a 10M context window for seamless long document analysis. Llama 4 MaverickIndustry-leading natively multimodal model for image and text understanding with groundbreaking intelligence and fast responses at a low cost. We evaluated model performance on a suite of common benchmarks across a wide range of languages, testing for coding, reasoning, knowledge, vision understanding, multilinguality, and long context. 4. Specialized long context evals are not traditionally reported for generalist models, so we share internal runs to showcase llama's frontier performance. 4. Specialized long context evals are not traditionally reported for generalist models, so we share internal runs to showcase llama's frontier performance.\", \"score\": 0.629889, \"raw_content\": null}]}",
|
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"error_message": null,
|
||||
|
|
|
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|
|
@ -0,0 +1,413 @@
|
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{
|
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"test_id": "tests/integration/responses/test_tool_responses.py::test_response_non_streaming_multi_turn_tool_execution[openai_client-txt=openai/gpt-4o-user_file_access_check]",
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"request": {
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"url": "https://api.openai.com/v1/v1/chat/completions",
|
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"headers": {},
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"body": {
|
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"model": "gpt-4o",
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"messages": [
|
||||
{
|
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"role": "user",
|
||||
"content": "I need to check if user 'alice' can access the file 'document.txt'. First, get alice's user ID, then check if that user ID can access the file 'document.txt'. Do this as a series of steps, where each step is a separate message. Return only one tool call per step. Summarize the final result with a single 'yes' or 'no' response."
|
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}
|
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],
|
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"stream": true,
|
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"stream_options": {
|
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"include_usage": true
|
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},
|
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"tools": [
|
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{
|
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"type": "function",
|
||||
"function": {
|
||||
"name": "get_user_id",
|
||||
"description": "\n Get the user ID for a given username. This ID is needed for other operations.\n\n :param username: The username to look up\n :return: The user ID for the username\n ",
|
||||
"parameters": {
|
||||
"properties": {
|
||||
"username": {
|
||||
"title": "Username",
|
||||
"type": "string"
|
||||
}
|
||||
},
|
||||
"required": [
|
||||
"username"
|
||||
],
|
||||
"title": "get_user_idArguments",
|
||||
"type": "object"
|
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}
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}
|
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},
|
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{
|
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"type": "function",
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"function": {
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"name": "get_user_permissions",
|
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"parameters": {
|
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"properties": {
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"user_id": {
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"title": "User Id",
|
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|
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}
|
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},
|
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"required": [
|
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"user_id"
|
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],
|
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"title": "get_user_permissionsArguments",
|
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"type": "object"
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}
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},
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{
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"type": "function",
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"name": "check_file_access",
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|
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"parameters": {
|
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"properties": {
|
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"user_id": {
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"title": "User Id",
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"type": "string"
|
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},
|
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"filename": {
|
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"title": "Filename",
|
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"type": "string"
|
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}
|
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},
|
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"required": [
|
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"user_id",
|
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"filename"
|
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],
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"title": "check_file_accessArguments",
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"type": "object"
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}
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}
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},
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{
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"type": "function",
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"function": {
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"name": "get_experiment_id",
|
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|
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"parameters": {
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"properties": {
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"experiment_name": {
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"title": "Experiment Name",
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"type": "string"
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}
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},
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"required": [
|
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"experiment_name"
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],
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"title": "get_experiment_idArguments",
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"type": "object"
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}
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}
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},
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{
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"type": "function",
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"function": {
|
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"name": "get_experiment_results",
|
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"description": "\n Get the results for an experiment ID. Requires a valid experiment ID from get_experiment_id.\n\n :param experiment_id: The experiment ID to get results for\n :return: The experiment results\n ",
|
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"parameters": {
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"properties": {
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"title": "Experiment Id",
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}
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},
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"required": [
|
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"experiment_id"
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],
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"title": "get_experiment_resultsArguments",
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"type": "object"
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}
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}
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}
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]
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},
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"endpoint": "/v1/chat/completions",
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"model": "gpt-4o"
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"response": {
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|
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|
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|
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|
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|
||||
"name": null
|
||||
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|
||||
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|
||||
}
|
||||
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|
||||
},
|
||||
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|
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"index": 0,
|
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|
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|
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|
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|
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|
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|
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|
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|
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{
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"usage": {
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"prompt_tokens": 454,
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"completion_tokens_details": {
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"prompt_tokens_details": {
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],
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"is_streaming": true
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},
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"id_normalization_mapping": {}
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}
|
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|
|
@ -0,0 +1,586 @@
|
|||
{
|
||||
"test_id": "tests/integration/responses/test_tool_responses.py::test_response_streaming_multi_turn_tool_execution[openai_client-txt=openai/gpt-4o-user_permissions_workflow]",
|
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"request": {
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"method": "POST",
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"url": "https://api.openai.com/v1/v1/chat/completions",
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"headers": {},
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"model": "gpt-4o",
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"messages": [
|
||||
{
|
||||
"role": "user",
|
||||
"content": "Help me with this security check: First, get the user ID for 'charlie', then get the permissions for that user ID, and finally check if that user can access 'secret_file.txt'. Stream your progress as you work through each step. Return only one tool call per step. Summarize the final result with a single 'yes' or 'no' response."
|
||||
},
|
||||
{
|
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"role": "assistant",
|
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"content": "",
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|
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"system_fingerprint": "fp_cbf1785567",
|
||||
"usage": {
|
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"completion_tokens": 18,
|
||||
"prompt_tokens": 195,
|
||||
"total_tokens": 213,
|
||||
"completion_tokens_details": {
|
||||
"accepted_prediction_tokens": 0,
|
||||
"audio_tokens": 0,
|
||||
"reasoning_tokens": 0,
|
||||
"rejected_prediction_tokens": 0
|
||||
},
|
||||
"prompt_tokens_details": {
|
||||
"audio_tokens": 0,
|
||||
"cached_tokens": 0
|
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|
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|
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"obfuscation": "psE6Es6zZ2Kz4"
|
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}
|
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}
|
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],
|
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"is_streaming": true
|
||||
},
|
||||
"id_normalization_mapping": {}
|
||||
}
|
||||
|
|
@ -13,8 +13,8 @@ from .streaming_assertions import StreamingValidator
|
|||
|
||||
|
||||
@pytest.mark.parametrize("case", basic_test_cases)
|
||||
def test_response_non_streaming_basic(compat_client, text_model_id, case):
|
||||
response = compat_client.responses.create(
|
||||
def test_response_non_streaming_basic(responses_client, text_model_id, case):
|
||||
response = responses_client.responses.create(
|
||||
model=text_model_id,
|
||||
input=case.input,
|
||||
stream=False,
|
||||
|
|
@ -31,10 +31,10 @@ def test_response_non_streaming_basic(compat_client, text_model_id, case):
|
|||
"Total tokens should equal input + output tokens"
|
||||
)
|
||||
|
||||
retrieved_response = compat_client.responses.retrieve(response_id=response.id)
|
||||
retrieved_response = responses_client.responses.retrieve(response_id=response.id)
|
||||
assert retrieved_response.output_text == response.output_text
|
||||
|
||||
next_response = compat_client.responses.create(
|
||||
next_response = responses_client.responses.create(
|
||||
model=text_model_id,
|
||||
input="Repeat your previous response in all caps.",
|
||||
previous_response_id=response.id,
|
||||
|
|
@ -44,8 +44,8 @@ def test_response_non_streaming_basic(compat_client, text_model_id, case):
|
|||
|
||||
|
||||
@pytest.mark.parametrize("case", basic_test_cases)
|
||||
def test_response_streaming_basic(compat_client, text_model_id, case):
|
||||
response = compat_client.responses.create(
|
||||
def test_response_streaming_basic(responses_client, text_model_id, case):
|
||||
response = responses_client.responses.create(
|
||||
model=text_model_id,
|
||||
input=case.input,
|
||||
stream=True,
|
||||
|
|
@ -98,15 +98,15 @@ def test_response_streaming_basic(compat_client, text_model_id, case):
|
|||
validator.assert_response_consistency()
|
||||
|
||||
# Verify stored response matches streamed response
|
||||
retrieved_response = compat_client.responses.retrieve(response_id=response_id)
|
||||
retrieved_response = responses_client.responses.retrieve(response_id=response_id)
|
||||
final_event = events[-1]
|
||||
assert retrieved_response.output_text == final_event.response.output_text
|
||||
|
||||
|
||||
@pytest.mark.parametrize("case", basic_test_cases)
|
||||
def test_response_streaming_incremental_content(compat_client, text_model_id, case):
|
||||
def test_response_streaming_incremental_content(responses_client, text_model_id, case):
|
||||
"""Test that streaming actually delivers content incrementally, not just at the end."""
|
||||
response = compat_client.responses.create(
|
||||
response = responses_client.responses.create(
|
||||
model=text_model_id,
|
||||
input=case.input,
|
||||
stream=True,
|
||||
|
|
@ -170,10 +170,10 @@ def test_response_streaming_incremental_content(compat_client, text_model_id, ca
|
|||
|
||||
|
||||
@pytest.mark.parametrize("case", multi_turn_test_cases)
|
||||
def test_response_non_streaming_multi_turn(compat_client, text_model_id, case):
|
||||
def test_response_non_streaming_multi_turn(responses_client, text_model_id, case):
|
||||
previous_response_id = None
|
||||
for turn_input, turn_expected in case.turns:
|
||||
response = compat_client.responses.create(
|
||||
response = responses_client.responses.create(
|
||||
model=text_model_id,
|
||||
input=turn_input,
|
||||
previous_response_id=previous_response_id,
|
||||
|
|
@ -184,8 +184,8 @@ def test_response_non_streaming_multi_turn(compat_client, text_model_id, case):
|
|||
|
||||
|
||||
@pytest.mark.parametrize("case", image_test_cases)
|
||||
def test_response_non_streaming_image(compat_client, text_model_id, case):
|
||||
response = compat_client.responses.create(
|
||||
def test_response_non_streaming_image(responses_client, text_model_id, case):
|
||||
response = responses_client.responses.create(
|
||||
model=text_model_id,
|
||||
input=case.input,
|
||||
stream=False,
|
||||
|
|
@ -195,10 +195,10 @@ def test_response_non_streaming_image(compat_client, text_model_id, case):
|
|||
|
||||
|
||||
@pytest.mark.parametrize("case", multi_turn_image_test_cases)
|
||||
def test_response_non_streaming_multi_turn_image(compat_client, text_model_id, case):
|
||||
def test_response_non_streaming_multi_turn_image(responses_client, text_model_id, case):
|
||||
previous_response_id = None
|
||||
for turn_input, turn_expected in case.turns:
|
||||
response = compat_client.responses.create(
|
||||
response = responses_client.responses.create(
|
||||
model=text_model_id,
|
||||
input=turn_input,
|
||||
previous_response_id=previous_response_id,
|
||||
|
|
|
|||
|
|
@ -131,18 +131,18 @@ class TestConversationResponses:
|
|||
assert len(response.output_text.strip()) > 0
|
||||
|
||||
# this is not ready yet
|
||||
# def test_conversation_compat_client(self, compat_client, text_model_id):
|
||||
# def test_conversation_compat_client(self, responses_client, text_model_id):
|
||||
# """Test conversation parameter works with compatibility client."""
|
||||
# if not hasattr(compat_client, "conversations"):
|
||||
# pytest.skip("compat_client does not support conversations API")
|
||||
# if not hasattr(responses_client, "conversations"):
|
||||
# pytest.skip("responses_client does not support conversations API")
|
||||
#
|
||||
# conversation = compat_client.conversations.create()
|
||||
# response = compat_client.responses.create(
|
||||
# conversation = responses_client.conversations.create()
|
||||
# response = responses_client.responses.create(
|
||||
# model=text_model_id, input="Tell me a joke", conversation=conversation.id
|
||||
# )
|
||||
#
|
||||
# assert response is not None
|
||||
# assert len(response.output_text.strip()) > 0
|
||||
#
|
||||
# conversation_items = compat_client.conversations.items.list(conversation.id)
|
||||
# conversation_items = responses_client.conversations.items.list(conversation.id)
|
||||
# assert len(conversation_items.data) >= 2
|
||||
|
|
|
|||
|
|
@ -9,8 +9,6 @@ import time
|
|||
|
||||
import pytest
|
||||
|
||||
from llama_stack.core.library_client import LlamaStackAsLibraryClient
|
||||
|
||||
from .helpers import new_vector_store, upload_file
|
||||
|
||||
|
||||
|
|
@ -28,12 +26,9 @@ from .helpers import new_vector_store, upload_file
|
|||
},
|
||||
],
|
||||
)
|
||||
def test_response_text_format(compat_client, text_model_id, text_format):
|
||||
if isinstance(compat_client, LlamaStackAsLibraryClient):
|
||||
pytest.skip("Responses API text format is not yet supported in library client.")
|
||||
|
||||
def test_response_text_format(responses_client, text_model_id, text_format):
|
||||
stream = False
|
||||
response = compat_client.responses.create(
|
||||
response = responses_client.responses.create(
|
||||
model=text_model_id,
|
||||
input="What is the capital of France?",
|
||||
stream=stream,
|
||||
|
|
@ -47,13 +42,10 @@ def test_response_text_format(compat_client, text_model_id, text_format):
|
|||
|
||||
|
||||
@pytest.fixture
|
||||
def vector_store_with_filtered_files(compat_client, embedding_model_id, embedding_dimension, tmp_path_factory):
|
||||
def vector_store_with_filtered_files(responses_client, embedding_model_id, embedding_dimension, tmp_path_factory):
|
||||
# """Create a vector store with multiple files that have different attributes for filtering tests."""
|
||||
if isinstance(compat_client, LlamaStackAsLibraryClient):
|
||||
pytest.skip("upload_file() is not yet supported in library client somehow?")
|
||||
|
||||
vector_store = new_vector_store(
|
||||
compat_client, "test_vector_store_with_filters", embedding_model_id, embedding_dimension
|
||||
responses_client, "test_vector_store_with_filters", embedding_model_id, embedding_dimension
|
||||
)
|
||||
tmp_path = tmp_path_factory.mktemp("filter_test_files")
|
||||
|
||||
|
|
@ -104,11 +96,11 @@ def vector_store_with_filtered_files(compat_client, embedding_model_id, embeddin
|
|||
file_path.write_text(file_data["content"])
|
||||
|
||||
# Upload file
|
||||
file_response = upload_file(compat_client, file_data["name"], str(file_path))
|
||||
file_response = upload_file(responses_client, file_data["name"], str(file_path))
|
||||
file_ids.append(file_response.id)
|
||||
|
||||
# Attach file to vector store with attributes
|
||||
file_attach_response = compat_client.vector_stores.files.create(
|
||||
file_attach_response = responses_client.vector_stores.files.create(
|
||||
vector_store_id=vector_store.id,
|
||||
file_id=file_response.id,
|
||||
attributes=file_data["attributes"],
|
||||
|
|
@ -117,7 +109,7 @@ def vector_store_with_filtered_files(compat_client, embedding_model_id, embeddin
|
|||
# Wait for attachment
|
||||
while file_attach_response.status == "in_progress":
|
||||
time.sleep(0.1)
|
||||
file_attach_response = compat_client.vector_stores.files.retrieve(
|
||||
file_attach_response = responses_client.vector_stores.files.retrieve(
|
||||
vector_store_id=vector_store.id,
|
||||
file_id=file_response.id,
|
||||
)
|
||||
|
|
@ -127,17 +119,17 @@ def vector_store_with_filtered_files(compat_client, embedding_model_id, embeddin
|
|||
|
||||
# Cleanup: delete vector store and files
|
||||
try:
|
||||
compat_client.vector_stores.delete(vector_store_id=vector_store.id)
|
||||
responses_client.vector_stores.delete(vector_store_id=vector_store.id)
|
||||
for file_id in file_ids:
|
||||
try:
|
||||
compat_client.files.delete(file_id=file_id)
|
||||
responses_client.files.delete(file_id=file_id)
|
||||
except Exception:
|
||||
pass # File might already be deleted
|
||||
except Exception:
|
||||
pass # Best effort cleanup
|
||||
|
||||
|
||||
def test_response_file_search_filter_by_region(compat_client, text_model_id, vector_store_with_filtered_files):
|
||||
def test_response_file_search_filter_by_region(responses_client, text_model_id, vector_store_with_filtered_files):
|
||||
"""Test file search with region equality filter."""
|
||||
tools = [
|
||||
{
|
||||
|
|
@ -147,7 +139,7 @@ def test_response_file_search_filter_by_region(compat_client, text_model_id, vec
|
|||
}
|
||||
]
|
||||
|
||||
response = compat_client.responses.create(
|
||||
response = responses_client.responses.create(
|
||||
model=text_model_id,
|
||||
input="What are the updates from the US region?",
|
||||
tools=tools,
|
||||
|
|
@ -168,7 +160,7 @@ def test_response_file_search_filter_by_region(compat_client, text_model_id, vec
|
|||
assert "asia" not in result.text.lower()
|
||||
|
||||
|
||||
def test_response_file_search_filter_by_category(compat_client, text_model_id, vector_store_with_filtered_files):
|
||||
def test_response_file_search_filter_by_category(responses_client, text_model_id, vector_store_with_filtered_files):
|
||||
"""Test file search with category equality filter."""
|
||||
tools = [
|
||||
{
|
||||
|
|
@ -178,7 +170,7 @@ def test_response_file_search_filter_by_category(compat_client, text_model_id, v
|
|||
}
|
||||
]
|
||||
|
||||
response = compat_client.responses.create(
|
||||
response = responses_client.responses.create(
|
||||
model=text_model_id,
|
||||
input="Show me all marketing reports",
|
||||
tools=tools,
|
||||
|
|
@ -198,7 +190,7 @@ def test_response_file_search_filter_by_category(compat_client, text_model_id, v
|
|||
assert "revenue figures" not in result.text.lower()
|
||||
|
||||
|
||||
def test_response_file_search_filter_by_date_range(compat_client, text_model_id, vector_store_with_filtered_files):
|
||||
def test_response_file_search_filter_by_date_range(responses_client, text_model_id, vector_store_with_filtered_files):
|
||||
"""Test file search with date range filter using compound AND."""
|
||||
tools = [
|
||||
{
|
||||
|
|
@ -222,7 +214,7 @@ def test_response_file_search_filter_by_date_range(compat_client, text_model_id,
|
|||
}
|
||||
]
|
||||
|
||||
response = compat_client.responses.create(
|
||||
response = responses_client.responses.create(
|
||||
model=text_model_id,
|
||||
input="What happened in Q1 2023?",
|
||||
tools=tools,
|
||||
|
|
@ -241,7 +233,7 @@ def test_response_file_search_filter_by_date_range(compat_client, text_model_id,
|
|||
assert "q3" not in result.text.lower()
|
||||
|
||||
|
||||
def test_response_file_search_filter_compound_and(compat_client, text_model_id, vector_store_with_filtered_files):
|
||||
def test_response_file_search_filter_compound_and(responses_client, text_model_id, vector_store_with_filtered_files):
|
||||
"""Test file search with compound AND filter (region AND category)."""
|
||||
tools = [
|
||||
{
|
||||
|
|
@ -257,7 +249,7 @@ def test_response_file_search_filter_compound_and(compat_client, text_model_id,
|
|||
}
|
||||
]
|
||||
|
||||
response = compat_client.responses.create(
|
||||
response = responses_client.responses.create(
|
||||
model=text_model_id,
|
||||
input="What are the engineering updates from the US?",
|
||||
tools=tools,
|
||||
|
|
@ -277,7 +269,7 @@ def test_response_file_search_filter_compound_and(compat_client, text_model_id,
|
|||
assert "promotional" not in result.text.lower() and "revenue" not in result.text.lower()
|
||||
|
||||
|
||||
def test_response_file_search_filter_compound_or(compat_client, text_model_id, vector_store_with_filtered_files):
|
||||
def test_response_file_search_filter_compound_or(responses_client, text_model_id, vector_store_with_filtered_files):
|
||||
"""Test file search with compound OR filter (marketing OR sales)."""
|
||||
tools = [
|
||||
{
|
||||
|
|
@ -293,7 +285,7 @@ def test_response_file_search_filter_compound_or(compat_client, text_model_id, v
|
|||
}
|
||||
]
|
||||
|
||||
response = compat_client.responses.create(
|
||||
response = responses_client.responses.create(
|
||||
model=text_model_id,
|
||||
input="Show me marketing and sales documents",
|
||||
tools=tools,
|
||||
|
|
@ -320,7 +312,7 @@ def test_response_file_search_filter_compound_or(compat_client, text_model_id, v
|
|||
assert categories_found.issubset({"marketing", "sales"}), f"Found unexpected categories: {categories_found}"
|
||||
|
||||
|
||||
def test_response_file_search_streaming_events(compat_client, text_model_id, vector_store_with_filtered_files):
|
||||
def test_response_file_search_streaming_events(responses_client, text_model_id, vector_store_with_filtered_files):
|
||||
"""Test that file search emits proper streaming events (in_progress, searching, completed)."""
|
||||
tools = [
|
||||
{
|
||||
|
|
@ -329,7 +321,7 @@ def test_response_file_search_streaming_events(compat_client, text_model_id, vec
|
|||
}
|
||||
]
|
||||
|
||||
stream = compat_client.responses.create(
|
||||
stream = responses_client.responses.create(
|
||||
model=text_model_id,
|
||||
input="What are the marketing updates?",
|
||||
tools=tools,
|
||||
|
|
|
|||
|
|
@ -9,6 +9,7 @@ import logging # allow-direct-logging
|
|||
import os
|
||||
|
||||
import httpx
|
||||
import llama_stack_client
|
||||
import openai
|
||||
import pytest
|
||||
|
||||
|
|
@ -29,8 +30,8 @@ from .streaming_assertions import StreamingValidator
|
|||
|
||||
|
||||
@pytest.mark.parametrize("case", web_search_test_cases)
|
||||
def test_response_non_streaming_web_search(compat_client, text_model_id, case):
|
||||
response = compat_client.responses.create(
|
||||
def test_response_non_streaming_web_search(responses_client, text_model_id, case):
|
||||
response = responses_client.responses.create(
|
||||
model=text_model_id,
|
||||
input=case.input,
|
||||
tools=case.tools,
|
||||
|
|
@ -48,12 +49,9 @@ def test_response_non_streaming_web_search(compat_client, text_model_id, case):
|
|||
|
||||
@pytest.mark.parametrize("case", file_search_test_cases)
|
||||
def test_response_non_streaming_file_search(
|
||||
compat_client, text_model_id, embedding_model_id, embedding_dimension, tmp_path, case
|
||||
responses_client, text_model_id, embedding_model_id, embedding_dimension, tmp_path, case
|
||||
):
|
||||
if isinstance(compat_client, LlamaStackAsLibraryClient):
|
||||
pytest.skip("Responses API file search is not yet supported in library client.")
|
||||
|
||||
vector_store = new_vector_store(compat_client, "test_vector_store", embedding_model_id, embedding_dimension)
|
||||
vector_store = new_vector_store(responses_client, "test_vector_store", embedding_model_id, embedding_dimension)
|
||||
|
||||
if case.file_content:
|
||||
file_name = "test_response_non_streaming_file_search.txt"
|
||||
|
|
@ -65,16 +63,16 @@ def test_response_non_streaming_file_search(
|
|||
else:
|
||||
raise ValueError("No file content or path provided for case")
|
||||
|
||||
file_response = upload_file(compat_client, file_name, file_path)
|
||||
file_response = upload_file(responses_client, file_name, file_path)
|
||||
|
||||
# Attach our file to the vector store
|
||||
compat_client.vector_stores.files.create(
|
||||
responses_client.vector_stores.files.create(
|
||||
vector_store_id=vector_store.id,
|
||||
file_id=file_response.id,
|
||||
)
|
||||
|
||||
# Wait for the file to be attached
|
||||
wait_for_file_attachment(compat_client, vector_store.id, file_response.id)
|
||||
wait_for_file_attachment(responses_client, vector_store.id, file_response.id)
|
||||
|
||||
# Update our tools with the right vector store id
|
||||
tools = case.tools
|
||||
|
|
@ -83,7 +81,7 @@ def test_response_non_streaming_file_search(
|
|||
tool["vector_store_ids"] = [vector_store.id]
|
||||
|
||||
# Create the response request, which should query our vector store
|
||||
response = compat_client.responses.create(
|
||||
response = responses_client.responses.create(
|
||||
model=text_model_id,
|
||||
input=case.input,
|
||||
tools=tools,
|
||||
|
|
@ -105,15 +103,12 @@ def test_response_non_streaming_file_search(
|
|||
|
||||
|
||||
def test_response_non_streaming_file_search_empty_vector_store(
|
||||
compat_client, text_model_id, embedding_model_id, embedding_dimension
|
||||
responses_client, text_model_id, embedding_model_id, embedding_dimension
|
||||
):
|
||||
if isinstance(compat_client, LlamaStackAsLibraryClient):
|
||||
pytest.skip("Responses API file search is not yet supported in library client.")
|
||||
|
||||
vector_store = new_vector_store(compat_client, "test_vector_store", embedding_model_id, embedding_dimension)
|
||||
vector_store = new_vector_store(responses_client, "test_vector_store", embedding_model_id, embedding_dimension)
|
||||
|
||||
# Create the response request, which should query our vector store
|
||||
response = compat_client.responses.create(
|
||||
response = responses_client.responses.create(
|
||||
model=text_model_id,
|
||||
input="How many experts does the Llama 4 Maverick model have?",
|
||||
tools=[{"type": "file_search", "vector_store_ids": [vector_store.id]}],
|
||||
|
|
@ -133,13 +128,10 @@ def test_response_non_streaming_file_search_empty_vector_store(
|
|||
|
||||
|
||||
def test_response_sequential_file_search(
|
||||
compat_client, text_model_id, embedding_model_id, embedding_dimension, tmp_path
|
||||
responses_client, text_model_id, embedding_model_id, embedding_dimension, tmp_path
|
||||
):
|
||||
"""Test file search with sequential responses using previous_response_id."""
|
||||
if isinstance(compat_client, LlamaStackAsLibraryClient):
|
||||
pytest.skip("Responses API file search is not yet supported in library client.")
|
||||
|
||||
vector_store = new_vector_store(compat_client, "test_vector_store", embedding_model_id, embedding_dimension)
|
||||
vector_store = new_vector_store(responses_client, "test_vector_store", embedding_model_id, embedding_dimension)
|
||||
|
||||
# Create a test file with content
|
||||
file_content = "The Llama 4 Maverick model has 128 experts in its mixture of experts architecture."
|
||||
|
|
@ -147,21 +139,21 @@ def test_response_sequential_file_search(
|
|||
file_path = tmp_path / file_name
|
||||
file_path.write_text(file_content)
|
||||
|
||||
file_response = upload_file(compat_client, file_name, file_path)
|
||||
file_response = upload_file(responses_client, file_name, file_path)
|
||||
|
||||
# Attach the file to the vector store
|
||||
compat_client.vector_stores.files.create(
|
||||
responses_client.vector_stores.files.create(
|
||||
vector_store_id=vector_store.id,
|
||||
file_id=file_response.id,
|
||||
)
|
||||
|
||||
# Wait for the file to be attached
|
||||
wait_for_file_attachment(compat_client, vector_store.id, file_response.id)
|
||||
wait_for_file_attachment(responses_client, vector_store.id, file_response.id)
|
||||
|
||||
tools = [{"type": "file_search", "vector_store_ids": [vector_store.id]}]
|
||||
|
||||
# First response request with file search
|
||||
response = compat_client.responses.create(
|
||||
response = responses_client.responses.create(
|
||||
model=text_model_id,
|
||||
input="How many experts does the Llama 4 Maverick model have?",
|
||||
tools=tools,
|
||||
|
|
@ -178,7 +170,7 @@ def test_response_sequential_file_search(
|
|||
assert "128" in response.output_text or "experts" in response.output_text.lower()
|
||||
|
||||
# Second response request using previous_response_id
|
||||
response2 = compat_client.responses.create(
|
||||
response2 = responses_client.responses.create(
|
||||
model=text_model_id,
|
||||
input="Can you tell me more about the architecture?",
|
||||
tools=tools,
|
||||
|
|
@ -199,14 +191,11 @@ def test_response_sequential_file_search(
|
|||
|
||||
|
||||
@pytest.mark.parametrize("case", mcp_tool_test_cases)
|
||||
def test_response_non_streaming_mcp_tool(compat_client, text_model_id, case, caplog):
|
||||
if not isinstance(compat_client, LlamaStackAsLibraryClient):
|
||||
pytest.skip("in-process MCP server is only supported in library client")
|
||||
|
||||
def test_response_non_streaming_mcp_tool(responses_client, text_model_id, case, caplog):
|
||||
with make_mcp_server() as mcp_server_info:
|
||||
tools = setup_mcp_tools(case.tools, mcp_server_info)
|
||||
|
||||
response = compat_client.responses.create(
|
||||
response = responses_client.responses.create(
|
||||
model=text_model_id,
|
||||
input=case.input,
|
||||
tools=tools,
|
||||
|
|
@ -243,15 +232,15 @@ def test_response_non_streaming_mcp_tool(compat_client, text_model_id, case, cap
|
|||
|
||||
exc_type = (
|
||||
AuthenticationRequiredError
|
||||
if isinstance(compat_client, LlamaStackAsLibraryClient)
|
||||
else (httpx.HTTPStatusError, openai.AuthenticationError)
|
||||
if isinstance(responses_client, LlamaStackAsLibraryClient)
|
||||
else (httpx.HTTPStatusError, openai.AuthenticationError, llama_stack_client.AuthenticationError)
|
||||
)
|
||||
# Suppress expected auth error logs only for the failing auth attempt
|
||||
with caplog.at_level(
|
||||
logging.CRITICAL, logger="llama_stack.providers.inline.agents.meta_reference.responses.streaming"
|
||||
):
|
||||
with pytest.raises(exc_type):
|
||||
compat_client.responses.create(
|
||||
responses_client.responses.create(
|
||||
model=text_model_id,
|
||||
input=case.input,
|
||||
tools=tools,
|
||||
|
|
@ -262,7 +251,7 @@ def test_response_non_streaming_mcp_tool(compat_client, text_model_id, case, cap
|
|||
if tool["type"] == "mcp":
|
||||
tool["headers"] = {"Authorization": "Bearer test-token"}
|
||||
|
||||
response = compat_client.responses.create(
|
||||
response = responses_client.responses.create(
|
||||
model=text_model_id,
|
||||
input=case.input,
|
||||
tools=tools,
|
||||
|
|
@ -272,14 +261,11 @@ def test_response_non_streaming_mcp_tool(compat_client, text_model_id, case, cap
|
|||
|
||||
|
||||
@pytest.mark.parametrize("case", mcp_tool_test_cases)
|
||||
def test_response_sequential_mcp_tool(compat_client, text_model_id, case):
|
||||
if not isinstance(compat_client, LlamaStackAsLibraryClient):
|
||||
pytest.skip("in-process MCP server is only supported in library client")
|
||||
|
||||
def test_response_sequential_mcp_tool(responses_client, text_model_id, case):
|
||||
with make_mcp_server() as mcp_server_info:
|
||||
tools = setup_mcp_tools(case.tools, mcp_server_info)
|
||||
|
||||
response = compat_client.responses.create(
|
||||
response = responses_client.responses.create(
|
||||
model=text_model_id,
|
||||
input=case.input,
|
||||
tools=tools,
|
||||
|
|
@ -311,7 +297,7 @@ def test_response_sequential_mcp_tool(compat_client, text_model_id, case):
|
|||
text_content = message.content[0].text
|
||||
assert "boiling point" in text_content.lower()
|
||||
|
||||
response2 = compat_client.responses.create(
|
||||
response2 = responses_client.responses.create(
|
||||
model=text_model_id, input=case.input, tools=tools, stream=False, previous_response_id=response.id
|
||||
)
|
||||
|
||||
|
|
@ -323,16 +309,13 @@ def test_response_sequential_mcp_tool(compat_client, text_model_id, case):
|
|||
|
||||
@pytest.mark.parametrize("case", mcp_tool_test_cases)
|
||||
@pytest.mark.parametrize("approve", [True, False])
|
||||
def test_response_mcp_tool_approval(compat_client, text_model_id, case, approve):
|
||||
if not isinstance(compat_client, LlamaStackAsLibraryClient):
|
||||
pytest.skip("in-process MCP server is only supported in library client")
|
||||
|
||||
def test_response_mcp_tool_approval(responses_client, text_model_id, case, approve):
|
||||
with make_mcp_server() as mcp_server_info:
|
||||
tools = setup_mcp_tools(case.tools, mcp_server_info)
|
||||
for tool in tools:
|
||||
tool["require_approval"] = "always"
|
||||
|
||||
response = compat_client.responses.create(
|
||||
response = responses_client.responses.create(
|
||||
model=text_model_id,
|
||||
input=case.input,
|
||||
tools=tools,
|
||||
|
|
@ -352,13 +335,13 @@ def test_response_mcp_tool_approval(compat_client, text_model_id, case, approve)
|
|||
approval_request = response.output[1]
|
||||
assert approval_request.type == "mcp_approval_request"
|
||||
assert approval_request.name == "get_boiling_point"
|
||||
assert json.loads(approval_request.arguments) == {
|
||||
"liquid_name": "myawesomeliquid",
|
||||
"celsius": True,
|
||||
}
|
||||
args = json.loads(approval_request.arguments)
|
||||
assert args["liquid_name"] == "myawesomeliquid"
|
||||
# celsius has a default value of True, so it may be omitted or explicitly set
|
||||
assert args.get("celsius", True) is True
|
||||
|
||||
# send approval response
|
||||
response = compat_client.responses.create(
|
||||
response = responses_client.responses.create(
|
||||
previous_response_id=response.id,
|
||||
model=text_model_id,
|
||||
input=[{"type": "mcp_approval_response", "approval_request_id": approval_request.id, "approve": approve}],
|
||||
|
|
@ -398,8 +381,8 @@ def test_response_mcp_tool_approval(compat_client, text_model_id, case, approve)
|
|||
|
||||
|
||||
@pytest.mark.parametrize("case", custom_tool_test_cases)
|
||||
def test_response_non_streaming_custom_tool(compat_client, text_model_id, case):
|
||||
response = compat_client.responses.create(
|
||||
def test_response_non_streaming_custom_tool(responses_client, text_model_id, case):
|
||||
response = responses_client.responses.create(
|
||||
model=text_model_id,
|
||||
input=case.input,
|
||||
tools=case.tools,
|
||||
|
|
@ -412,8 +395,8 @@ def test_response_non_streaming_custom_tool(compat_client, text_model_id, case):
|
|||
|
||||
|
||||
@pytest.mark.parametrize("case", custom_tool_test_cases)
|
||||
def test_response_function_call_ordering_1(compat_client, text_model_id, case):
|
||||
response = compat_client.responses.create(
|
||||
def test_response_function_call_ordering_1(responses_client, text_model_id, case):
|
||||
response = responses_client.responses.create(
|
||||
model=text_model_id,
|
||||
input=case.input,
|
||||
tools=case.tools,
|
||||
|
|
@ -437,13 +420,13 @@ def test_response_function_call_ordering_1(compat_client, text_model_id, case):
|
|||
"call_id": response.output[0].call_id,
|
||||
}
|
||||
)
|
||||
response = compat_client.responses.create(
|
||||
response = responses_client.responses.create(
|
||||
model=text_model_id, input=inputs, tools=case.tools, stream=False, previous_response_id=response.id
|
||||
)
|
||||
assert len(response.output) == 1
|
||||
|
||||
|
||||
def test_response_function_call_ordering_2(compat_client, text_model_id):
|
||||
def test_response_function_call_ordering_2(responses_client, text_model_id):
|
||||
tools = [
|
||||
{
|
||||
"type": "function",
|
||||
|
|
@ -468,7 +451,7 @@ def test_response_function_call_ordering_2(compat_client, text_model_id):
|
|||
"content": "Is the weather better in San Francisco or Los Angeles?",
|
||||
}
|
||||
]
|
||||
response = compat_client.responses.create(
|
||||
response = responses_client.responses.create(
|
||||
model=text_model_id,
|
||||
input=inputs,
|
||||
tools=tools,
|
||||
|
|
@ -489,7 +472,7 @@ def test_response_function_call_ordering_2(compat_client, text_model_id):
|
|||
"call_id": output.call_id,
|
||||
}
|
||||
)
|
||||
response = compat_client.responses.create(
|
||||
response = responses_client.responses.create(
|
||||
model=text_model_id,
|
||||
input=inputs,
|
||||
tools=tools,
|
||||
|
|
@ -500,15 +483,12 @@ def test_response_function_call_ordering_2(compat_client, text_model_id):
|
|||
|
||||
|
||||
@pytest.mark.parametrize("case", multi_turn_tool_execution_test_cases)
|
||||
def test_response_non_streaming_multi_turn_tool_execution(compat_client, text_model_id, case):
|
||||
def test_response_non_streaming_multi_turn_tool_execution(responses_client, text_model_id, case):
|
||||
"""Test multi-turn tool execution where multiple MCP tool calls are performed in sequence."""
|
||||
if not isinstance(compat_client, LlamaStackAsLibraryClient):
|
||||
pytest.skip("in-process MCP server is only supported in library client")
|
||||
|
||||
with make_mcp_server(tools=dependency_tools()) as mcp_server_info:
|
||||
tools = setup_mcp_tools(case.tools, mcp_server_info)
|
||||
|
||||
response = compat_client.responses.create(
|
||||
response = responses_client.responses.create(
|
||||
input=case.input,
|
||||
model=text_model_id,
|
||||
tools=tools,
|
||||
|
|
@ -550,15 +530,12 @@ def test_response_non_streaming_multi_turn_tool_execution(compat_client, text_mo
|
|||
|
||||
|
||||
@pytest.mark.parametrize("case", multi_turn_tool_execution_streaming_test_cases)
|
||||
def test_response_streaming_multi_turn_tool_execution(compat_client, text_model_id, case):
|
||||
def test_response_streaming_multi_turn_tool_execution(responses_client, text_model_id, case):
|
||||
"""Test streaming multi-turn tool execution where multiple MCP tool calls are performed in sequence."""
|
||||
if not isinstance(compat_client, LlamaStackAsLibraryClient):
|
||||
pytest.skip("in-process MCP server is only supported in library client")
|
||||
|
||||
with make_mcp_server(tools=dependency_tools()) as mcp_server_info:
|
||||
tools = setup_mcp_tools(case.tools, mcp_server_info)
|
||||
|
||||
stream = compat_client.responses.create(
|
||||
stream = responses_client.responses.create(
|
||||
input=case.input,
|
||||
model=text_model_id,
|
||||
tools=tools,
|
||||
|
|
|
|||
|
|
@ -13,8 +13,8 @@ from collections.abc import Generator
|
|||
|
||||
import pytest
|
||||
|
||||
from llama_stack.apis.safety import ViolationLevel
|
||||
from llama_stack.models.llama.sku_types import CoreModelId
|
||||
from llama_stack_api import ViolationLevel
|
||||
|
||||
# Llama Guard models available for text and vision shields
|
||||
LLAMA_GUARD_TEXT_MODELS = [CoreModelId.llama_guard_4_12b.value]
|
||||
|
|
|
|||
|
|
@ -8,7 +8,7 @@ import mimetypes
|
|||
|
||||
import pytest
|
||||
|
||||
from llama_stack.apis.safety import ViolationLevel
|
||||
from llama_stack_api import ViolationLevel
|
||||
|
||||
CODE_SCANNER_ENABLED_PROVIDERS = {"ollama", "together", "fireworks"}
|
||||
|
||||
|
|
|
|||
|
|
@ -10,7 +10,7 @@ import os
|
|||
|
||||
import pytest
|
||||
|
||||
from llama_stack.apis.safety import ViolationLevel
|
||||
from llama_stack_api import ViolationLevel
|
||||
|
||||
VISION_SHIELD_ENABLED_PROVIDERS = {"together"}
|
||||
|
||||
|
|
|
|||
|
|
@ -10,8 +10,6 @@ import pytest
|
|||
from llama_stack_client.lib.agents.agent import Agent
|
||||
from llama_stack_client.lib.agents.turn_events import StepCompleted, StepProgress, ToolCallIssuedDelta
|
||||
|
||||
from llama_stack.core.library_client import LlamaStackAsLibraryClient
|
||||
|
||||
AUTH_TOKEN = "test-token"
|
||||
|
||||
from tests.common.mcp import MCP_TOOLGROUP_ID, make_mcp_server
|
||||
|
|
@ -24,9 +22,6 @@ def mcp_server():
|
|||
|
||||
|
||||
def test_mcp_invocation(llama_stack_client, text_model_id, mcp_server):
|
||||
if not isinstance(llama_stack_client, LlamaStackAsLibraryClient):
|
||||
pytest.skip("The local MCP server only reliably reachable from library client.")
|
||||
|
||||
test_toolgroup_id = MCP_TOOLGROUP_ID
|
||||
uri = mcp_server["server_url"]
|
||||
|
||||
|
|
|
|||
|
|
@ -8,8 +8,8 @@ import re
|
|||
|
||||
import pytest
|
||||
|
||||
from llama_stack.apis.common.errors import ToolGroupNotFoundError
|
||||
from llama_stack.core.library_client import LlamaStackAsLibraryClient
|
||||
from llama_stack_api import ToolGroupNotFoundError
|
||||
from tests.common.mcp import MCP_TOOLGROUP_ID, make_mcp_server
|
||||
|
||||
|
||||
|
|
|
|||
|
|
@ -11,10 +11,9 @@ import pytest
|
|||
from llama_stack_client import BadRequestError
|
||||
from openai import BadRequestError as OpenAIBadRequestError
|
||||
|
||||
from llama_stack.apis.files import ExpiresAfter
|
||||
from llama_stack.apis.vector_io import Chunk
|
||||
from llama_stack.core.library_client import LlamaStackAsLibraryClient
|
||||
from llama_stack.log import get_logger
|
||||
from llama_stack_api import Chunk, ExpiresAfter
|
||||
|
||||
from ..conftest import vector_provider_wrapper
|
||||
|
||||
|
|
@ -646,7 +645,7 @@ def test_openai_vector_store_attach_file(
|
|||
):
|
||||
"""Test OpenAI vector store attach file."""
|
||||
skip_if_provider_doesnt_support_openai_vector_stores(client_with_models)
|
||||
from llama_stack.apis.files import ExpiresAfter
|
||||
from llama_stack_api import ExpiresAfter
|
||||
|
||||
compat_client = compat_client_with_empty_stores
|
||||
|
||||
|
|
@ -710,7 +709,7 @@ def test_openai_vector_store_attach_files_on_creation(
|
|||
skip_if_provider_doesnt_support_openai_vector_stores(client_with_models)
|
||||
|
||||
compat_client = compat_client_with_empty_stores
|
||||
from llama_stack.apis.files import ExpiresAfter
|
||||
from llama_stack_api import ExpiresAfter
|
||||
|
||||
# Create some files and attach them to the vector store
|
||||
valid_file_ids = []
|
||||
|
|
@ -775,7 +774,7 @@ def test_openai_vector_store_list_files(
|
|||
skip_if_provider_doesnt_support_openai_vector_stores(client_with_models)
|
||||
|
||||
compat_client = compat_client_with_empty_stores
|
||||
from llama_stack.apis.files import ExpiresAfter
|
||||
from llama_stack_api import ExpiresAfter
|
||||
|
||||
# Create a vector store
|
||||
vector_store = compat_client.vector_stores.create(
|
||||
|
|
@ -867,7 +866,7 @@ def test_openai_vector_store_retrieve_file_contents(
|
|||
skip_if_provider_doesnt_support_openai_vector_stores(client_with_models)
|
||||
|
||||
compat_client = compat_client_with_empty_stores
|
||||
from llama_stack.apis.files import ExpiresAfter
|
||||
from llama_stack_api import ExpiresAfter
|
||||
|
||||
# Create a vector store
|
||||
vector_store = compat_client.vector_stores.create(
|
||||
|
|
@ -928,7 +927,7 @@ def test_openai_vector_store_delete_file(
|
|||
skip_if_provider_doesnt_support_openai_vector_stores(client_with_models)
|
||||
|
||||
compat_client = compat_client_with_empty_stores
|
||||
from llama_stack.apis.files import ExpiresAfter
|
||||
from llama_stack_api import ExpiresAfter
|
||||
|
||||
# Create a vector store
|
||||
vector_store = compat_client.vector_stores.create(
|
||||
|
|
@ -994,7 +993,7 @@ def test_openai_vector_store_delete_file_removes_from_vector_store(
|
|||
skip_if_provider_doesnt_support_openai_vector_stores(client_with_models)
|
||||
|
||||
compat_client = compat_client_with_empty_stores
|
||||
from llama_stack.apis.files import ExpiresAfter
|
||||
from llama_stack_api import ExpiresAfter
|
||||
|
||||
# Create a vector store
|
||||
vector_store = compat_client.vector_stores.create(
|
||||
|
|
@ -1046,7 +1045,7 @@ def test_openai_vector_store_update_file(
|
|||
skip_if_provider_doesnt_support_openai_vector_stores(client_with_models)
|
||||
|
||||
compat_client = compat_client_with_empty_stores
|
||||
from llama_stack.apis.files import ExpiresAfter
|
||||
from llama_stack_api import ExpiresAfter
|
||||
|
||||
# Create a vector store
|
||||
vector_store = compat_client.vector_stores.create(
|
||||
|
|
@ -1103,7 +1102,7 @@ def test_create_vector_store_files_duplicate_vector_store_name(
|
|||
This test confirms that client.vector_stores.create() creates a unique ID
|
||||
"""
|
||||
skip_if_provider_doesnt_support_openai_vector_stores(client_with_models)
|
||||
from llama_stack.apis.files import ExpiresAfter
|
||||
from llama_stack_api import ExpiresAfter
|
||||
|
||||
compat_client = compat_client_with_empty_stores
|
||||
|
||||
|
|
|
|||
|
|
@ -6,7 +6,7 @@
|
|||
|
||||
import pytest
|
||||
|
||||
from llama_stack.apis.vector_io import Chunk
|
||||
from llama_stack_api import Chunk
|
||||
|
||||
from ..conftest import vector_provider_wrapper
|
||||
|
||||
|
|
|
|||
|
|
@ -5,11 +5,7 @@
|
|||
# the root directory of this source tree.
|
||||
|
||||
|
||||
from llama_stack.apis.conversations.conversations import (
|
||||
Conversation,
|
||||
ConversationItem,
|
||||
ConversationItemList,
|
||||
)
|
||||
from llama_stack_api import Conversation, ConversationItem, ConversationItemList
|
||||
|
||||
|
||||
def test_conversation_model_defaults():
|
||||
|
|
|
|||
|
|
@ -12,10 +12,6 @@ from openai.types.conversations.conversation import Conversation as OpenAIConver
|
|||
from openai.types.conversations.conversation_item import ConversationItem as OpenAIConversationItem
|
||||
from pydantic import TypeAdapter
|
||||
|
||||
from llama_stack.apis.agents.openai_responses import (
|
||||
OpenAIResponseInputMessageContentText,
|
||||
OpenAIResponseMessage,
|
||||
)
|
||||
from llama_stack.core.conversations.conversations import (
|
||||
ConversationServiceConfig,
|
||||
ConversationServiceImpl,
|
||||
|
|
@ -28,6 +24,7 @@ from llama_stack.core.storage.datatypes import (
|
|||
StorageConfig,
|
||||
)
|
||||
from llama_stack.providers.utils.sqlstore.sqlstore import register_sqlstore_backends
|
||||
from llama_stack_api import OpenAIResponseInputMessageContentText, OpenAIResponseMessage
|
||||
|
||||
|
||||
@pytest.fixture
|
||||
|
|
|
|||
|
|
@ -6,10 +6,9 @@
|
|||
|
||||
from unittest.mock import AsyncMock
|
||||
|
||||
from llama_stack.apis.safety.safety import ModerationObject, ModerationObjectResults
|
||||
from llama_stack.apis.shields import ListShieldsResponse, Shield
|
||||
from llama_stack.core.datatypes import SafetyConfig
|
||||
from llama_stack.core.routers.safety import SafetyRouter
|
||||
from llama_stack_api import ListShieldsResponse, ModerationObject, ModerationObjectResults, Shield
|
||||
|
||||
|
||||
async def test_run_moderation_uses_default_shield_when_model_missing():
|
||||
|
|
|
|||
|
|
@ -8,8 +8,13 @@ from unittest.mock import AsyncMock, Mock
|
|||
|
||||
import pytest
|
||||
|
||||
from llama_stack.apis.vector_io import OpenAICreateVectorStoreRequestWithExtraBody
|
||||
from llama_stack.core.routers.vector_io import VectorIORouter
|
||||
from llama_stack_api import (
|
||||
ModelNotFoundError,
|
||||
ModelType,
|
||||
ModelTypeError,
|
||||
OpenAICreateVectorStoreRequestWithExtraBody,
|
||||
)
|
||||
|
||||
|
||||
async def test_single_provider_auto_selection():
|
||||
|
|
@ -21,6 +26,7 @@ async def test_single_provider_auto_selection():
|
|||
Mock(identifier="all-MiniLM-L6-v2", model_type="embedding", metadata={"embedding_dimension": 384})
|
||||
]
|
||||
)
|
||||
mock_routing_table.get_object_by_identifier = AsyncMock(return_value=Mock(model_type=ModelType.embedding))
|
||||
mock_routing_table.register_vector_store = AsyncMock(
|
||||
return_value=Mock(identifier="vs_123", provider_id="inline::faiss", provider_resource_id="vs_123")
|
||||
)
|
||||
|
|
@ -48,6 +54,7 @@ async def test_create_vector_stores_multiple_providers_missing_provider_id_error
|
|||
Mock(identifier="all-MiniLM-L6-v2", model_type="embedding", metadata={"embedding_dimension": 384})
|
||||
]
|
||||
)
|
||||
mock_routing_table.get_object_by_identifier = AsyncMock(return_value=Mock(model_type=ModelType.embedding))
|
||||
router = VectorIORouter(mock_routing_table)
|
||||
request = OpenAICreateVectorStoreRequestWithExtraBody.model_validate(
|
||||
{"name": "test_store", "embedding_model": "all-MiniLM-L6-v2"}
|
||||
|
|
@ -117,3 +124,32 @@ async def test_update_vector_store_same_provider_id_succeeds():
|
|||
provider.openai_update_vector_store.assert_called_once_with(
|
||||
vector_store_id="vs_123", name="updated_name", expires_after=None, metadata={"provider_id": "inline::faiss"}
|
||||
)
|
||||
|
||||
|
||||
async def test_create_vector_store_with_unknown_embedding_model_raises_error():
|
||||
"""Test that creating a vector store with an unknown embedding model raises
|
||||
FoundError."""
|
||||
mock_routing_table = Mock(impls_by_provider_id={"provider": "mock"})
|
||||
mock_routing_table.get_object_by_identifier = AsyncMock(return_value=None)
|
||||
|
||||
router = VectorIORouter(mock_routing_table)
|
||||
request = OpenAICreateVectorStoreRequestWithExtraBody.model_validate(
|
||||
{"embedding_model": "unknown-model", "embedding_dimension": 384}
|
||||
)
|
||||
|
||||
with pytest.raises(ModelNotFoundError, match="Model 'unknown-model' not found"):
|
||||
await router.openai_create_vector_store(request)
|
||||
|
||||
|
||||
async def test_create_vector_store_with_wrong_model_type_raises_error():
|
||||
"""Test that creating a vector store with a non-embedding model raises ModelTypeError."""
|
||||
mock_routing_table = Mock(impls_by_provider_id={"provider": "mock"})
|
||||
mock_routing_table.get_object_by_identifier = AsyncMock(return_value=Mock(model_type=ModelType.llm))
|
||||
|
||||
router = VectorIORouter(mock_routing_table)
|
||||
request = OpenAICreateVectorStoreRequestWithExtraBody.model_validate(
|
||||
{"embedding_model": "text-model", "embedding_dimension": 384}
|
||||
)
|
||||
|
||||
with pytest.raises(ModelTypeError, match="Model 'text-model' is of type"):
|
||||
await router.openai_create_vector_store(request)
|
||||
|
|
|
|||
|
|
@ -10,11 +10,9 @@ from unittest.mock import AsyncMock
|
|||
|
||||
import pytest
|
||||
|
||||
from llama_stack.apis.models import ListModelsResponse, Model, ModelType
|
||||
from llama_stack.apis.shields import ListShieldsResponse, Shield
|
||||
from llama_stack.core.datatypes import QualifiedModel, SafetyConfig, StackRunConfig, StorageConfig, VectorStoresConfig
|
||||
from llama_stack.core.stack import validate_safety_config, validate_vector_stores_config
|
||||
from llama_stack.providers.datatypes import Api
|
||||
from llama_stack_api import Api, ListModelsResponse, ListShieldsResponse, Model, ModelType, Shield
|
||||
|
||||
|
||||
class TestVectorStoresValidation:
|
||||
|
|
|
|||
|
|
@ -10,14 +10,6 @@ from unittest.mock import AsyncMock
|
|||
|
||||
import pytest
|
||||
|
||||
from llama_stack.apis.common.content_types import URL
|
||||
from llama_stack.apis.common.errors import ModelNotFoundError
|
||||
from llama_stack.apis.common.type_system import NumberType
|
||||
from llama_stack.apis.datasets.datasets import Dataset, DatasetPurpose, URIDataSource
|
||||
from llama_stack.apis.datatypes import Api
|
||||
from llama_stack.apis.models import Model, ModelType
|
||||
from llama_stack.apis.shields.shields import Shield
|
||||
from llama_stack.apis.tools import ListToolDefsResponse, ToolDef, ToolGroup
|
||||
from llama_stack.core.datatypes import RegistryEntrySource
|
||||
from llama_stack.core.routing_tables.benchmarks import BenchmarksRoutingTable
|
||||
from llama_stack.core.routing_tables.datasets import DatasetsRoutingTable
|
||||
|
|
@ -25,6 +17,21 @@ from llama_stack.core.routing_tables.models import ModelsRoutingTable
|
|||
from llama_stack.core.routing_tables.scoring_functions import ScoringFunctionsRoutingTable
|
||||
from llama_stack.core.routing_tables.shields import ShieldsRoutingTable
|
||||
from llama_stack.core.routing_tables.toolgroups import ToolGroupsRoutingTable
|
||||
from llama_stack_api import (
|
||||
URL,
|
||||
Api,
|
||||
Dataset,
|
||||
DatasetPurpose,
|
||||
ListToolDefsResponse,
|
||||
Model,
|
||||
ModelNotFoundError,
|
||||
ModelType,
|
||||
NumberType,
|
||||
Shield,
|
||||
ToolDef,
|
||||
ToolGroup,
|
||||
URIDataSource,
|
||||
)
|
||||
|
||||
|
||||
class Impl:
|
||||
|
|
|
|||
|
|
@ -11,8 +11,15 @@ from unittest.mock import patch
|
|||
import pytest
|
||||
from openai import AsyncOpenAI
|
||||
|
||||
from llama_stack.testing.api_recorder import (
|
||||
APIRecordingMode,
|
||||
ResponseStorage,
|
||||
api_recording,
|
||||
normalize_inference_request,
|
||||
)
|
||||
|
||||
# Import the real Pydantic response types instead of using Mocks
|
||||
from llama_stack.apis.inference import (
|
||||
from llama_stack_api import (
|
||||
OpenAIAssistantMessageParam,
|
||||
OpenAIChatCompletion,
|
||||
OpenAIChoice,
|
||||
|
|
@ -20,12 +27,6 @@ from llama_stack.apis.inference import (
|
|||
OpenAIEmbeddingsResponse,
|
||||
OpenAIEmbeddingUsage,
|
||||
)
|
||||
from llama_stack.testing.api_recorder import (
|
||||
APIRecordingMode,
|
||||
ResponseStorage,
|
||||
api_recording,
|
||||
normalize_inference_request,
|
||||
)
|
||||
|
||||
|
||||
@pytest.fixture
|
||||
|
|
|
|||
|
|
@ -22,7 +22,7 @@ from llama_stack.core.storage.datatypes import (
|
|||
SqlStoreReference,
|
||||
StorageConfig,
|
||||
)
|
||||
from llama_stack.providers.datatypes import ProviderSpec
|
||||
from llama_stack_api import ProviderSpec
|
||||
|
||||
|
||||
class SampleConfig(BaseModel):
|
||||
|
|
@ -312,7 +312,7 @@ pip_packages:
|
|||
"""Test loading an external provider from a module (success path)."""
|
||||
from types import SimpleNamespace
|
||||
|
||||
from llama_stack.providers.datatypes import Api, ProviderSpec
|
||||
from llama_stack_api import Api, ProviderSpec
|
||||
|
||||
# Simulate a provider module with get_provider_spec
|
||||
fake_spec = ProviderSpec(
|
||||
|
|
@ -396,7 +396,7 @@ pip_packages:
|
|||
def test_external_provider_from_module_building(self, mock_providers):
|
||||
"""Test loading an external provider from a module during build (building=True, partial spec)."""
|
||||
from llama_stack.core.datatypes import BuildConfig, BuildProvider, DistributionSpec
|
||||
from llama_stack.providers.datatypes import Api
|
||||
from llama_stack_api import Api
|
||||
|
||||
# No importlib patch needed, should not import module when type of `config` is BuildConfig or DistributionSpec
|
||||
build_config = BuildConfig(
|
||||
|
|
@ -457,7 +457,7 @@ class TestGetExternalProvidersFromModule:
|
|||
from types import SimpleNamespace
|
||||
|
||||
from llama_stack.core.distribution import get_external_providers_from_module
|
||||
from llama_stack.providers.datatypes import ProviderSpec
|
||||
from llama_stack_api import ProviderSpec
|
||||
|
||||
fake_spec = ProviderSpec(
|
||||
api=Api.inference,
|
||||
|
|
@ -594,7 +594,7 @@ class TestGetExternalProvidersFromModule:
|
|||
from types import SimpleNamespace
|
||||
|
||||
from llama_stack.core.distribution import get_external_providers_from_module
|
||||
from llama_stack.providers.datatypes import ProviderSpec
|
||||
from llama_stack_api import ProviderSpec
|
||||
|
||||
spec1 = ProviderSpec(
|
||||
api=Api.inference,
|
||||
|
|
@ -642,7 +642,7 @@ class TestGetExternalProvidersFromModule:
|
|||
from types import SimpleNamespace
|
||||
|
||||
from llama_stack.core.distribution import get_external_providers_from_module
|
||||
from llama_stack.providers.datatypes import ProviderSpec
|
||||
from llama_stack_api import ProviderSpec
|
||||
|
||||
spec1 = ProviderSpec(
|
||||
api=Api.inference,
|
||||
|
|
@ -690,7 +690,7 @@ class TestGetExternalProvidersFromModule:
|
|||
from types import SimpleNamespace
|
||||
|
||||
from llama_stack.core.distribution import get_external_providers_from_module
|
||||
from llama_stack.providers.datatypes import ProviderSpec
|
||||
from llama_stack_api import ProviderSpec
|
||||
|
||||
# Module returns both inline and remote variants
|
||||
spec1 = ProviderSpec(
|
||||
|
|
@ -829,7 +829,7 @@ class TestGetExternalProvidersFromModule:
|
|||
from types import SimpleNamespace
|
||||
|
||||
from llama_stack.core.distribution import get_external_providers_from_module
|
||||
from llama_stack.providers.datatypes import ProviderSpec
|
||||
from llama_stack_api import ProviderSpec
|
||||
|
||||
inference_spec = ProviderSpec(
|
||||
api=Api.inference,
|
||||
|
|
|
|||
|
|
@ -7,9 +7,6 @@
|
|||
|
||||
import pytest
|
||||
|
||||
from llama_stack.apis.common.errors import ResourceNotFoundError
|
||||
from llama_stack.apis.common.responses import Order
|
||||
from llama_stack.apis.files import OpenAIFilePurpose
|
||||
from llama_stack.core.access_control.access_control import default_policy
|
||||
from llama_stack.core.storage.datatypes import SqliteSqlStoreConfig, SqlStoreReference
|
||||
from llama_stack.providers.inline.files.localfs import (
|
||||
|
|
@ -17,6 +14,7 @@ from llama_stack.providers.inline.files.localfs import (
|
|||
LocalfsFilesImplConfig,
|
||||
)
|
||||
from llama_stack.providers.utils.sqlstore.sqlstore import register_sqlstore_backends
|
||||
from llama_stack_api import OpenAIFilePurpose, Order, ResourceNotFoundError
|
||||
|
||||
|
||||
class MockUploadFile:
|
||||
|
|
|
|||
|
|
@ -59,8 +59,7 @@ from unittest.mock import AsyncMock, MagicMock
|
|||
|
||||
import pytest
|
||||
|
||||
from llama_stack.apis.batches import BatchObject
|
||||
from llama_stack.apis.common.errors import ConflictError, ResourceNotFoundError
|
||||
from llama_stack_api import BatchObject, ConflictError, ResourceNotFoundError
|
||||
|
||||
|
||||
class TestReferenceBatchesImpl:
|
||||
|
|
|
|||
|
|
@ -44,7 +44,7 @@ import asyncio
|
|||
|
||||
import pytest
|
||||
|
||||
from llama_stack.apis.common.errors import ConflictError
|
||||
from llama_stack_api import ConflictError
|
||||
|
||||
|
||||
class TestReferenceBatchesIdempotency:
|
||||
|
|
|
|||
|
|
@ -9,8 +9,7 @@ from unittest.mock import patch
|
|||
import pytest
|
||||
from botocore.exceptions import ClientError
|
||||
|
||||
from llama_stack.apis.common.errors import ResourceNotFoundError
|
||||
from llama_stack.apis.files import OpenAIFilePurpose
|
||||
from llama_stack_api import OpenAIFilePurpose, ResourceNotFoundError
|
||||
|
||||
|
||||
class TestS3FilesImpl:
|
||||
|
|
@ -228,7 +227,7 @@ class TestS3FilesImpl:
|
|||
|
||||
mock_now.return_value = 0
|
||||
|
||||
from llama_stack.apis.files import ExpiresAfter
|
||||
from llama_stack_api import ExpiresAfter
|
||||
|
||||
sample_text_file.filename = "test_expired_file"
|
||||
uploaded = await s3_provider.openai_upload_file(
|
||||
|
|
@ -260,7 +259,7 @@ class TestS3FilesImpl:
|
|||
|
||||
async def test_unsupported_expires_after_anchor(self, s3_provider, sample_text_file):
|
||||
"""Unsupported anchor value should raise ValueError."""
|
||||
from llama_stack.apis.files import ExpiresAfter
|
||||
from llama_stack_api import ExpiresAfter
|
||||
|
||||
sample_text_file.filename = "test_unsupported_expires_after_anchor"
|
||||
|
||||
|
|
@ -273,7 +272,7 @@ class TestS3FilesImpl:
|
|||
|
||||
async def test_nonint_expires_after_seconds(self, s3_provider, sample_text_file):
|
||||
"""Non-integer seconds in expires_after should raise ValueError."""
|
||||
from llama_stack.apis.files import ExpiresAfter
|
||||
from llama_stack_api import ExpiresAfter
|
||||
|
||||
sample_text_file.filename = "test_nonint_expires_after_seconds"
|
||||
|
||||
|
|
@ -286,7 +285,7 @@ class TestS3FilesImpl:
|
|||
|
||||
async def test_expires_after_seconds_out_of_bounds(self, s3_provider, sample_text_file):
|
||||
"""Seconds outside allowed range should raise ValueError."""
|
||||
from llama_stack.apis.files import ExpiresAfter
|
||||
from llama_stack_api import ExpiresAfter
|
||||
|
||||
with pytest.raises(ValueError, match="greater than or equal to 3600"):
|
||||
await s3_provider.openai_upload_file(
|
||||
|
|
|
|||
|
|
@ -8,10 +8,9 @@ from unittest.mock import patch
|
|||
|
||||
import pytest
|
||||
|
||||
from llama_stack.apis.common.errors import ResourceNotFoundError
|
||||
from llama_stack.apis.files import OpenAIFilePurpose
|
||||
from llama_stack.core.datatypes import User
|
||||
from llama_stack.providers.remote.files.s3.files import S3FilesImpl
|
||||
from llama_stack_api import OpenAIFilePurpose, ResourceNotFoundError
|
||||
|
||||
|
||||
async def test_listing_hides_other_users_file(s3_provider, sample_text_file):
|
||||
|
|
|
|||
|
|
@ -10,9 +10,9 @@ from unittest.mock import AsyncMock, MagicMock
|
|||
import pytest
|
||||
from openai import AuthenticationError
|
||||
|
||||
from llama_stack.apis.inference import OpenAIChatCompletionRequestWithExtraBody
|
||||
from llama_stack.providers.remote.inference.bedrock.bedrock import BedrockInferenceAdapter
|
||||
from llama_stack.providers.remote.inference.bedrock.config import BedrockConfig
|
||||
from llama_stack_api import OpenAIChatCompletionRequestWithExtraBody
|
||||
|
||||
|
||||
def test_adapter_initialization():
|
||||
|
|
|
|||
|
|
@ -10,7 +10,13 @@ from unittest.mock import AsyncMock, MagicMock, PropertyMock, patch
|
|||
|
||||
import pytest
|
||||
|
||||
from llama_stack.apis.inference import (
|
||||
from llama_stack.core.routers.inference import InferenceRouter
|
||||
from llama_stack.core.routing_tables.models import ModelsRoutingTable
|
||||
from llama_stack.providers.remote.inference.vllm.config import VLLMInferenceAdapterConfig
|
||||
from llama_stack.providers.remote.inference.vllm.vllm import VLLMInferenceAdapter
|
||||
from llama_stack_api import (
|
||||
HealthStatus,
|
||||
Model,
|
||||
OpenAIAssistantMessageParam,
|
||||
OpenAIChatCompletion,
|
||||
OpenAIChatCompletionRequestWithExtraBody,
|
||||
|
|
@ -20,12 +26,6 @@ from llama_stack.apis.inference import (
|
|||
OpenAICompletionRequestWithExtraBody,
|
||||
ToolChoice,
|
||||
)
|
||||
from llama_stack.apis.models import Model
|
||||
from llama_stack.core.routers.inference import InferenceRouter
|
||||
from llama_stack.core.routing_tables.models import ModelsRoutingTable
|
||||
from llama_stack.providers.datatypes import HealthStatus
|
||||
from llama_stack.providers.remote.inference.vllm.config import VLLMInferenceAdapterConfig
|
||||
from llama_stack.providers.remote.inference.vllm.vllm import VLLMInferenceAdapter
|
||||
|
||||
# These are unit test for the remote vllm provider
|
||||
# implementation. This should only contain tests which are specific to
|
||||
|
|
|
|||
|
|
@ -8,11 +8,11 @@ from unittest.mock import AsyncMock
|
|||
|
||||
import pytest
|
||||
|
||||
from llama_stack.apis.tools import ToolDef
|
||||
from llama_stack.providers.inline.agents.meta_reference.responses.streaming import (
|
||||
convert_tooldef_to_chat_tool,
|
||||
)
|
||||
from llama_stack.providers.inline.agents.meta_reference.responses.types import ChatCompletionContext
|
||||
from llama_stack_api import ToolDef
|
||||
|
||||
|
||||
@pytest.fixture
|
||||
|
|
|
|||
|
|
@ -9,10 +9,9 @@ from unittest.mock import patch
|
|||
|
||||
import pytest
|
||||
|
||||
from llama_stack.apis.datasets import Dataset, DatasetPurpose, URIDataSource
|
||||
from llama_stack.apis.resource import ResourceType
|
||||
from llama_stack.providers.remote.datasetio.nvidia.config import NvidiaDatasetIOConfig
|
||||
from llama_stack.providers.remote.datasetio.nvidia.datasetio import NvidiaDatasetIOAdapter
|
||||
from llama_stack_api import Dataset, DatasetPurpose, ResourceType, URIDataSource
|
||||
|
||||
|
||||
@pytest.fixture
|
||||
|
|
|
|||
|
|
@ -9,14 +9,20 @@ from unittest.mock import MagicMock, patch
|
|||
|
||||
import pytest
|
||||
|
||||
from llama_stack.apis.benchmarks import Benchmark
|
||||
from llama_stack.apis.common.job_types import Job, JobStatus
|
||||
from llama_stack.apis.eval.eval import BenchmarkConfig, EvaluateResponse, ModelCandidate, SamplingParams
|
||||
from llama_stack.apis.inference.inference import TopPSamplingStrategy
|
||||
from llama_stack.apis.resource import ResourceType
|
||||
from llama_stack.models.llama.sku_types import CoreModelId
|
||||
from llama_stack.providers.remote.eval.nvidia.config import NVIDIAEvalConfig
|
||||
from llama_stack.providers.remote.eval.nvidia.eval import NVIDIAEvalImpl
|
||||
from llama_stack_api import (
|
||||
Benchmark,
|
||||
BenchmarkConfig,
|
||||
EvaluateResponse,
|
||||
Job,
|
||||
JobStatus,
|
||||
ModelCandidate,
|
||||
ResourceType,
|
||||
SamplingParams,
|
||||
TopPSamplingStrategy,
|
||||
)
|
||||
|
||||
MOCK_DATASET_ID = "default/test-dataset"
|
||||
MOCK_BENCHMARK_ID = "test-benchmark"
|
||||
|
|
|
|||
|
|
@ -10,7 +10,12 @@ from unittest.mock import patch
|
|||
|
||||
import pytest
|
||||
|
||||
from llama_stack.apis.post_training.post_training import (
|
||||
from llama_stack.core.library_client import convert_pydantic_to_json_value
|
||||
from llama_stack.providers.remote.post_training.nvidia.post_training import (
|
||||
NvidiaPostTrainingAdapter,
|
||||
NvidiaPostTrainingConfig,
|
||||
)
|
||||
from llama_stack_api import (
|
||||
DataConfig,
|
||||
DatasetFormat,
|
||||
EfficiencyConfig,
|
||||
|
|
@ -19,11 +24,6 @@ from llama_stack.apis.post_training.post_training import (
|
|||
OptimizerType,
|
||||
TrainingConfig,
|
||||
)
|
||||
from llama_stack.core.library_client import convert_pydantic_to_json_value
|
||||
from llama_stack.providers.remote.post_training.nvidia.post_training import (
|
||||
NvidiaPostTrainingAdapter,
|
||||
NvidiaPostTrainingConfig,
|
||||
)
|
||||
|
||||
|
||||
class TestNvidiaParameters:
|
||||
|
|
|
|||
|
|
@ -9,10 +9,10 @@ from unittest.mock import AsyncMock, MagicMock, patch
|
|||
import aiohttp
|
||||
import pytest
|
||||
|
||||
from llama_stack.apis.models import ModelType
|
||||
from llama_stack.providers.remote.inference.nvidia.config import NVIDIAConfig
|
||||
from llama_stack.providers.remote.inference.nvidia.nvidia import NVIDIAInferenceAdapter
|
||||
from llama_stack.providers.utils.inference.openai_mixin import OpenAIMixin
|
||||
from llama_stack_api import ModelType
|
||||
|
||||
|
||||
class MockResponse:
|
||||
|
|
|
|||
|
|
@ -10,15 +10,16 @@ from unittest.mock import AsyncMock, MagicMock, patch
|
|||
|
||||
import pytest
|
||||
|
||||
from llama_stack.apis.inference import (
|
||||
OpenAIAssistantMessageParam,
|
||||
OpenAIUserMessageParam,
|
||||
)
|
||||
from llama_stack.apis.resource import ResourceType
|
||||
from llama_stack.apis.safety import RunShieldResponse, ViolationLevel
|
||||
from llama_stack.apis.shields import Shield
|
||||
from llama_stack.providers.remote.safety.nvidia.config import NVIDIASafetyConfig
|
||||
from llama_stack.providers.remote.safety.nvidia.nvidia import NVIDIASafetyAdapter
|
||||
from llama_stack_api import (
|
||||
OpenAIAssistantMessageParam,
|
||||
OpenAIUserMessageParam,
|
||||
ResourceType,
|
||||
RunShieldResponse,
|
||||
Shield,
|
||||
ViolationLevel,
|
||||
)
|
||||
|
||||
|
||||
class FakeNVIDIASafetyAdapter(NVIDIASafetyAdapter):
|
||||
|
|
|
|||
|
|
@ -10,15 +10,6 @@ from unittest.mock import patch
|
|||
|
||||
import pytest
|
||||
|
||||
from llama_stack.apis.post_training.post_training import (
|
||||
DataConfig,
|
||||
DatasetFormat,
|
||||
LoraFinetuningConfig,
|
||||
OptimizerConfig,
|
||||
OptimizerType,
|
||||
QATFinetuningConfig,
|
||||
TrainingConfig,
|
||||
)
|
||||
from llama_stack.core.library_client import convert_pydantic_to_json_value
|
||||
from llama_stack.providers.remote.post_training.nvidia.post_training import (
|
||||
ListNvidiaPostTrainingJobs,
|
||||
|
|
@ -27,6 +18,15 @@ from llama_stack.providers.remote.post_training.nvidia.post_training import (
|
|||
NvidiaPostTrainingJob,
|
||||
NvidiaPostTrainingJobStatusResponse,
|
||||
)
|
||||
from llama_stack_api import (
|
||||
DataConfig,
|
||||
DatasetFormat,
|
||||
LoraFinetuningConfig,
|
||||
OptimizerConfig,
|
||||
OptimizerType,
|
||||
QATFinetuningConfig,
|
||||
TrainingConfig,
|
||||
)
|
||||
|
||||
|
||||
@pytest.fixture
|
||||
|
|
|
|||
|
|
@ -7,9 +7,9 @@
|
|||
from types import SimpleNamespace
|
||||
from unittest.mock import AsyncMock, PropertyMock, patch
|
||||
|
||||
from llama_stack.apis.inference import OpenAIChatCompletionRequestWithExtraBody
|
||||
from llama_stack.providers.remote.inference.bedrock.bedrock import BedrockInferenceAdapter
|
||||
from llama_stack.providers.remote.inference.bedrock.config import BedrockConfig
|
||||
from llama_stack_api import OpenAIChatCompletionRequestWithExtraBody
|
||||
|
||||
|
||||
def test_can_create_adapter():
|
||||
|
|
|
|||
|
|
@ -12,11 +12,10 @@ from unittest.mock import AsyncMock, MagicMock, Mock, PropertyMock, patch
|
|||
import pytest
|
||||
from pydantic import BaseModel, Field
|
||||
|
||||
from llama_stack.apis.inference import Model, OpenAIChatCompletionRequestWithExtraBody, OpenAIUserMessageParam
|
||||
from llama_stack.apis.models import ModelType
|
||||
from llama_stack.core.request_headers import request_provider_data_context
|
||||
from llama_stack.providers.utils.inference.model_registry import RemoteInferenceProviderConfig
|
||||
from llama_stack.providers.utils.inference.openai_mixin import OpenAIMixin
|
||||
from llama_stack_api import Model, ModelType, OpenAIChatCompletionRequestWithExtraBody, OpenAIUserMessageParam
|
||||
|
||||
|
||||
class OpenAIMixinImpl(OpenAIMixin):
|
||||
|
|
|
|||
|
|
@ -4,14 +4,11 @@
|
|||
# This source code is licensed under the terms described in the LICENSE file in
|
||||
# the root directory of this source tree.
|
||||
|
||||
from llama_stack.apis.inference import (
|
||||
OpenAIAssistantMessageParam,
|
||||
OpenAIUserMessageParam,
|
||||
)
|
||||
from llama_stack.models.llama.datatypes import RawTextItem
|
||||
from llama_stack.providers.utils.inference.prompt_adapter import (
|
||||
convert_openai_message_to_raw_message,
|
||||
)
|
||||
from llama_stack_api import OpenAIAssistantMessageParam, OpenAIUserMessageParam
|
||||
|
||||
|
||||
class TestConvertOpenAIMessageToRawMessage:
|
||||
|
|
|
|||
|
|
@ -8,9 +8,8 @@ from unittest.mock import AsyncMock, MagicMock, patch
|
|||
|
||||
import pytest
|
||||
|
||||
from llama_stack.apis.common.content_types import URL, TextContentItem
|
||||
from llama_stack.apis.tools import RAGDocument
|
||||
from llama_stack.providers.utils.memory.vector_store import content_from_data_and_mime_type, content_from_doc
|
||||
from llama_stack_api import URL, RAGDocument, TextContentItem
|
||||
|
||||
|
||||
async def test_content_from_doc_with_url():
|
||||
|
|
|
|||
|
|
@ -35,8 +35,8 @@
|
|||
|
||||
import pytest
|
||||
|
||||
from llama_stack.apis.models import Model
|
||||
from llama_stack.providers.utils.inference.model_registry import ModelRegistryHelper, ProviderModelEntry
|
||||
from llama_stack_api import Model
|
||||
|
||||
|
||||
@pytest.fixture
|
||||
|
|
|
|||
|
|
@ -10,8 +10,6 @@ from unittest.mock import AsyncMock, MagicMock, patch
|
|||
import numpy as np
|
||||
import pytest
|
||||
|
||||
from llama_stack.apis.vector_io import Chunk, ChunkMetadata, QueryChunksResponse
|
||||
from llama_stack.apis.vector_stores import VectorStore
|
||||
from llama_stack.core.storage.datatypes import KVStoreReference, SqliteKVStoreConfig
|
||||
from llama_stack.providers.inline.vector_io.faiss.config import FaissVectorIOConfig
|
||||
from llama_stack.providers.inline.vector_io.faiss.faiss import FaissIndex, FaissVectorIOAdapter
|
||||
|
|
@ -20,6 +18,7 @@ from llama_stack.providers.inline.vector_io.sqlite_vec.sqlite_vec import SQLiteV
|
|||
from llama_stack.providers.remote.vector_io.pgvector.config import PGVectorVectorIOConfig
|
||||
from llama_stack.providers.remote.vector_io.pgvector.pgvector import PGVectorIndex, PGVectorVectorIOAdapter
|
||||
from llama_stack.providers.utils.kvstore import register_kvstore_backends
|
||||
from llama_stack_api import Chunk, ChunkMetadata, QueryChunksResponse, VectorStore
|
||||
|
||||
EMBEDDING_DIMENSION = 768
|
||||
COLLECTION_PREFIX = "test_collection"
|
||||
|
|
|
|||
|
|
@ -10,15 +10,12 @@ from unittest.mock import MagicMock, patch
|
|||
import numpy as np
|
||||
import pytest
|
||||
|
||||
from llama_stack.apis.files import Files
|
||||
from llama_stack.apis.vector_io import Chunk, QueryChunksResponse
|
||||
from llama_stack.apis.vector_stores import VectorStore
|
||||
from llama_stack.providers.datatypes import HealthStatus
|
||||
from llama_stack.providers.inline.vector_io.faiss.config import FaissVectorIOConfig
|
||||
from llama_stack.providers.inline.vector_io.faiss.faiss import (
|
||||
FaissIndex,
|
||||
FaissVectorIOAdapter,
|
||||
)
|
||||
from llama_stack_api import Chunk, Files, HealthStatus, QueryChunksResponse, VectorStore
|
||||
|
||||
# This test is a unit test for the FaissVectorIOAdapter class. This should only contain
|
||||
# tests which are specific to this class. More general (API-level) tests should be placed in
|
||||
|
|
|
|||
|
|
@ -9,12 +9,12 @@ import asyncio
|
|||
import numpy as np
|
||||
import pytest
|
||||
|
||||
from llama_stack.apis.vector_io import Chunk, QueryChunksResponse
|
||||
from llama_stack.providers.inline.vector_io.sqlite_vec.sqlite_vec import (
|
||||
SQLiteVecIndex,
|
||||
SQLiteVecVectorIOAdapter,
|
||||
_create_sqlite_connection,
|
||||
)
|
||||
from llama_stack_api import Chunk, QueryChunksResponse
|
||||
|
||||
# This test is a unit test for the SQLiteVecVectorIOAdapter class. This should only contain
|
||||
# tests which are specific to this class. More general (API-level) tests should be placed in
|
||||
|
|
|
|||
|
|
@ -11,17 +11,17 @@ from unittest.mock import AsyncMock, patch
|
|||
import numpy as np
|
||||
import pytest
|
||||
|
||||
from llama_stack.apis.common.errors import VectorStoreNotFoundError
|
||||
from llama_stack.apis.vector_io import (
|
||||
from llama_stack.providers.inline.vector_io.sqlite_vec.sqlite_vec import VECTOR_DBS_PREFIX
|
||||
from llama_stack_api import (
|
||||
Chunk,
|
||||
OpenAICreateVectorStoreFileBatchRequestWithExtraBody,
|
||||
OpenAICreateVectorStoreRequestWithExtraBody,
|
||||
QueryChunksResponse,
|
||||
VectorStore,
|
||||
VectorStoreChunkingStrategyAuto,
|
||||
VectorStoreFileObject,
|
||||
VectorStoreNotFoundError,
|
||||
)
|
||||
from llama_stack.apis.vector_stores import VectorStore
|
||||
from llama_stack.providers.inline.vector_io.sqlite_vec.sqlite_vec import VECTOR_DBS_PREFIX
|
||||
|
||||
# This test is a unit test for the inline VectorIO providers. This should only contain
|
||||
# tests which are specific to this class. More general (API-level) tests should be placed in
|
||||
|
|
@ -222,7 +222,7 @@ async def test_insert_chunks_missing_db_raises(vector_io_adapter):
|
|||
|
||||
async def test_insert_chunks_with_missing_document_id(vector_io_adapter):
|
||||
"""Ensure no KeyError when document_id is missing or in different places."""
|
||||
from llama_stack.apis.vector_io import Chunk, ChunkMetadata
|
||||
from llama_stack_api import Chunk, ChunkMetadata
|
||||
|
||||
fake_index = AsyncMock()
|
||||
vector_io_adapter.cache["db1"] = fake_index
|
||||
|
|
@ -255,10 +255,9 @@ async def test_insert_chunks_with_missing_document_id(vector_io_adapter):
|
|||
|
||||
async def test_document_id_with_invalid_type_raises_error():
|
||||
"""Ensure TypeError is raised when document_id is not a string."""
|
||||
from llama_stack.apis.vector_io import Chunk
|
||||
|
||||
# Integer document_id should raise TypeError
|
||||
from llama_stack.providers.utils.vector_io.vector_utils import generate_chunk_id
|
||||
from llama_stack_api import Chunk
|
||||
|
||||
chunk = Chunk(content="test", chunk_id=generate_chunk_id("test", "test"), metadata={"document_id": 12345})
|
||||
with pytest.raises(TypeError) as exc_info:
|
||||
|
|
|
|||
|
|
@ -4,8 +4,8 @@
|
|||
# This source code is licensed under the terms described in the LICENSE file in
|
||||
# the root directory of this source tree.
|
||||
|
||||
from llama_stack.apis.vector_io import Chunk, ChunkMetadata
|
||||
from llama_stack.providers.utils.vector_io.vector_utils import generate_chunk_id
|
||||
from llama_stack_api import Chunk, ChunkMetadata
|
||||
|
||||
# This test is a unit test for the chunk_utils.py helpers. This should only contain
|
||||
# tests which are specific to this file. More general (API-level) tests should be placed in
|
||||
|
|
|
|||
|
|
@ -8,13 +8,8 @@ from unittest.mock import AsyncMock, MagicMock
|
|||
|
||||
import pytest
|
||||
|
||||
from llama_stack.apis.tools.rag_tool import RAGQueryConfig
|
||||
from llama_stack.apis.vector_io import (
|
||||
Chunk,
|
||||
ChunkMetadata,
|
||||
QueryChunksResponse,
|
||||
)
|
||||
from llama_stack.providers.inline.tool_runtime.rag.memory import MemoryToolRuntimeImpl
|
||||
from llama_stack_api import Chunk, ChunkMetadata, QueryChunksResponse, RAGQueryConfig
|
||||
|
||||
|
||||
class TestRagQuery:
|
||||
|
|
|
|||
|
|
@ -13,12 +13,6 @@ from unittest.mock import AsyncMock, MagicMock
|
|||
import numpy as np
|
||||
import pytest
|
||||
|
||||
from llama_stack.apis.inference.inference import (
|
||||
OpenAIEmbeddingData,
|
||||
OpenAIEmbeddingsRequestWithExtraBody,
|
||||
)
|
||||
from llama_stack.apis.tools import RAGDocument
|
||||
from llama_stack.apis.vector_io import Chunk
|
||||
from llama_stack.providers.utils.memory.vector_store import (
|
||||
URL,
|
||||
VectorStoreWithIndex,
|
||||
|
|
@ -27,6 +21,7 @@ from llama_stack.providers.utils.memory.vector_store import (
|
|||
make_overlapped_chunks,
|
||||
)
|
||||
from llama_stack.providers.utils.vector_io.vector_utils import generate_chunk_id
|
||||
from llama_stack_api import Chunk, OpenAIEmbeddingData, OpenAIEmbeddingsRequestWithExtraBody, RAGDocument
|
||||
|
||||
DUMMY_PDF_PATH = Path(os.path.abspath(__file__)).parent / "fixtures" / "dummy.pdf"
|
||||
# Depending on the machine, this can get parsed a couple of ways
|
||||
|
|
|
|||
|
|
@ -7,8 +7,6 @@
|
|||
|
||||
import pytest
|
||||
|
||||
from llama_stack.apis.inference import Model
|
||||
from llama_stack.apis.vector_stores import VectorStore
|
||||
from llama_stack.core.datatypes import VectorStoreWithOwner
|
||||
from llama_stack.core.storage.datatypes import KVStoreReference, SqliteKVStoreConfig
|
||||
from llama_stack.core.store.registry import (
|
||||
|
|
@ -17,6 +15,7 @@ from llama_stack.core.store.registry import (
|
|||
DiskDistributionRegistry,
|
||||
)
|
||||
from llama_stack.providers.utils.kvstore import kvstore_impl, register_kvstore_backends
|
||||
from llama_stack_api import Model, VectorStore
|
||||
|
||||
|
||||
@pytest.fixture
|
||||
|
|
@ -304,8 +303,8 @@ async def test_double_registration_different_objects(disk_dist_registry):
|
|||
|
||||
async def test_double_registration_with_cache(cached_disk_dist_registry):
|
||||
"""Test double registration behavior with caching enabled."""
|
||||
from llama_stack.apis.models import ModelType
|
||||
from llama_stack.core.datatypes import ModelWithOwner
|
||||
from llama_stack_api import ModelType
|
||||
|
||||
model1 = ModelWithOwner(
|
||||
identifier="test_model",
|
||||
|
|
|
|||
|
|
@ -5,9 +5,9 @@
|
|||
# the root directory of this source tree.
|
||||
|
||||
|
||||
from llama_stack.apis.models import ModelType
|
||||
from llama_stack.core.datatypes import ModelWithOwner, User
|
||||
from llama_stack.core.store.registry import CachedDiskDistributionRegistry
|
||||
from llama_stack_api import ModelType
|
||||
|
||||
|
||||
async def test_registry_cache_with_acl(cached_disk_dist_registry):
|
||||
|
|
|
|||
|
|
@ -10,11 +10,10 @@ import pytest
|
|||
import yaml
|
||||
from pydantic import TypeAdapter, ValidationError
|
||||
|
||||
from llama_stack.apis.datatypes import Api
|
||||
from llama_stack.apis.models import ModelType
|
||||
from llama_stack.core.access_control.access_control import AccessDeniedError, is_action_allowed
|
||||
from llama_stack.core.datatypes import AccessRule, ModelWithOwner, User
|
||||
from llama_stack.core.routing_tables.models import ModelsRoutingTable
|
||||
from llama_stack_api import Api, ModelType
|
||||
|
||||
|
||||
class AsyncMock(MagicMock):
|
||||
|
|
|
|||
|
|
@ -144,7 +144,7 @@ def middleware_with_mocks(mock_auth_endpoint):
|
|||
middleware = AuthenticationMiddleware(mock_app, auth_config, {})
|
||||
|
||||
# Mock the route_impls to simulate finding routes with required scopes
|
||||
from llama_stack.schema_utils import WebMethod
|
||||
from llama_stack_api import WebMethod
|
||||
|
||||
routes = {
|
||||
("POST", "/test/scoped"): WebMethod(route="/test/scoped", method="POST", required_scope="test.read"),
|
||||
|
|
|
|||
|
|
@ -11,7 +11,6 @@ from unittest.mock import AsyncMock, MagicMock
|
|||
|
||||
from pydantic import BaseModel, Field
|
||||
|
||||
from llama_stack.apis.inference import Inference
|
||||
from llama_stack.core.datatypes import Api, Provider, StackRunConfig
|
||||
from llama_stack.core.resolver import resolve_impls
|
||||
from llama_stack.core.routers.inference import InferenceRouter
|
||||
|
|
@ -25,9 +24,9 @@ from llama_stack.core.storage.datatypes import (
|
|||
SqlStoreReference,
|
||||
StorageConfig,
|
||||
)
|
||||
from llama_stack.providers.datatypes import InlineProviderSpec, ProviderSpec
|
||||
from llama_stack.providers.utils.kvstore import register_kvstore_backends
|
||||
from llama_stack.providers.utils.sqlstore.sqlstore import register_sqlstore_backends
|
||||
from llama_stack_api import Inference, InlineProviderSpec, ProviderSpec
|
||||
|
||||
|
||||
def add_protocol_methods(cls: type, protocol: type[Protocol]) -> None:
|
||||
|
|
|
|||
|
|
@ -12,7 +12,7 @@ from pydantic import ValidationError
|
|||
|
||||
from llama_stack.core.access_control.access_control import AccessDeniedError
|
||||
from llama_stack.core.datatypes import AuthenticationRequiredError
|
||||
from llama_stack.core.server.server import translate_exception
|
||||
from llama_stack.core.server.server import remove_disabled_providers, translate_exception
|
||||
|
||||
|
||||
class TestTranslateException:
|
||||
|
|
@ -194,3 +194,70 @@ class TestTranslateException:
|
|||
assert isinstance(result3, HTTPException)
|
||||
assert result3.status_code == 403
|
||||
assert result3.detail == "Permission denied: Access denied"
|
||||
|
||||
|
||||
class TestRemoveDisabledProviders:
|
||||
"""Test cases for the remove_disabled_providers function."""
|
||||
|
||||
def test_remove_explicitly_disabled_provider(self):
|
||||
"""Test that providers with provider_id='__disabled__' are removed."""
|
||||
config = {
|
||||
"providers": {
|
||||
"inference": [
|
||||
{"provider_id": "openai", "provider_type": "remote::openai", "config": {}},
|
||||
{"provider_id": "__disabled__", "provider_type": "remote::vllm", "config": {}},
|
||||
]
|
||||
}
|
||||
}
|
||||
result = remove_disabled_providers(config)
|
||||
assert len(result["providers"]["inference"]) == 1
|
||||
assert result["providers"]["inference"][0]["provider_id"] == "openai"
|
||||
|
||||
def test_remove_empty_provider_id(self):
|
||||
"""Test that providers with empty provider_id are removed."""
|
||||
config = {
|
||||
"providers": {
|
||||
"inference": [
|
||||
{"provider_id": "openai", "provider_type": "remote::openai", "config": {}},
|
||||
{"provider_id": "", "provider_type": "remote::vllm", "config": {}},
|
||||
]
|
||||
}
|
||||
}
|
||||
result = remove_disabled_providers(config)
|
||||
assert len(result["providers"]["inference"]) == 1
|
||||
assert result["providers"]["inference"][0]["provider_id"] == "openai"
|
||||
|
||||
def test_keep_models_with_none_provider_model_id(self):
|
||||
"""Test that models with None provider_model_id are NOT removed."""
|
||||
config = {
|
||||
"registered_resources": {
|
||||
"models": [
|
||||
{
|
||||
"model_id": "llama-3-2-3b",
|
||||
"provider_id": "vllm-inference",
|
||||
"model_type": "llm",
|
||||
"provider_model_id": None,
|
||||
"metadata": {},
|
||||
},
|
||||
{
|
||||
"model_id": "gpt-4o-mini",
|
||||
"provider_id": "openai",
|
||||
"model_type": "llm",
|
||||
"provider_model_id": None,
|
||||
"metadata": {},
|
||||
},
|
||||
{
|
||||
"model_id": "granite-embedding-125m",
|
||||
"provider_id": "sentence-transformers",
|
||||
"model_type": "embedding",
|
||||
"provider_model_id": "ibm-granite/granite-embedding-125m-english",
|
||||
"metadata": {"embedding_dimension": 768},
|
||||
},
|
||||
]
|
||||
}
|
||||
}
|
||||
result = remove_disabled_providers(config)
|
||||
assert len(result["registered_resources"]["models"]) == 3
|
||||
assert result["registered_resources"]["models"][0]["model_id"] == "llama-3-2-3b"
|
||||
assert result["registered_resources"]["models"][1]["model_id"] == "gpt-4o-mini"
|
||||
assert result["registered_resources"]["models"][2]["model_id"] == "granite-embedding-125m"
|
||||
|
|
|
|||
|
|
@ -10,8 +10,8 @@ from unittest.mock import AsyncMock, MagicMock
|
|||
|
||||
import pytest
|
||||
|
||||
from llama_stack.apis.common.responses import PaginatedResponse
|
||||
from llama_stack.core.server.server import create_dynamic_typed_route, create_sse_event, sse_generator
|
||||
from llama_stack_api import PaginatedResponse
|
||||
|
||||
|
||||
@pytest.fixture
|
||||
|
|
|
|||
|
|
@ -11,8 +11,8 @@ Tests the new input_schema and output_schema fields.
|
|||
|
||||
from pydantic import ValidationError
|
||||
|
||||
from llama_stack.apis.tools import ToolDef
|
||||
from llama_stack.models.llama.datatypes import BuiltinTool, ToolDefinition
|
||||
from llama_stack_api import ToolDef
|
||||
|
||||
|
||||
class TestToolDefValidation:
|
||||
|
|
|
|||
|
|
@ -8,16 +8,16 @@ import time
|
|||
|
||||
import pytest
|
||||
|
||||
from llama_stack.apis.inference import (
|
||||
from llama_stack.core.storage.datatypes import InferenceStoreReference, SqliteSqlStoreConfig
|
||||
from llama_stack.providers.utils.inference.inference_store import InferenceStore
|
||||
from llama_stack.providers.utils.sqlstore.sqlstore import register_sqlstore_backends
|
||||
from llama_stack_api import (
|
||||
OpenAIAssistantMessageParam,
|
||||
OpenAIChatCompletion,
|
||||
OpenAIChoice,
|
||||
OpenAIUserMessageParam,
|
||||
Order,
|
||||
)
|
||||
from llama_stack.core.storage.datatypes import InferenceStoreReference, SqliteSqlStoreConfig
|
||||
from llama_stack.providers.utils.inference.inference_store import InferenceStore
|
||||
from llama_stack.providers.utils.sqlstore.sqlstore import register_sqlstore_backends
|
||||
|
||||
|
||||
@pytest.fixture(autouse=True)
|
||||
|
|
|
|||
|
|
@ -10,15 +10,10 @@ from uuid import uuid4
|
|||
|
||||
import pytest
|
||||
|
||||
from llama_stack.apis.agents import Order
|
||||
from llama_stack.apis.agents.openai_responses import (
|
||||
OpenAIResponseInput,
|
||||
OpenAIResponseObject,
|
||||
)
|
||||
from llama_stack.apis.inference import OpenAIMessageParam, OpenAIUserMessageParam
|
||||
from llama_stack.core.storage.datatypes import ResponsesStoreReference, SqliteSqlStoreConfig
|
||||
from llama_stack.providers.utils.responses.responses_store import ResponsesStore
|
||||
from llama_stack.providers.utils.sqlstore.sqlstore import register_sqlstore_backends
|
||||
from llama_stack_api import OpenAIMessageParam, OpenAIResponseInput, OpenAIResponseObject, OpenAIUserMessageParam, Order
|
||||
|
||||
|
||||
def build_store(db_path: str, policy: list | None = None) -> ResponsesStore:
|
||||
|
|
@ -46,7 +41,7 @@ def create_test_response_object(
|
|||
|
||||
def create_test_response_input(content: str, input_id: str) -> OpenAIResponseInput:
|
||||
"""Helper to create a test response input."""
|
||||
from llama_stack.apis.agents.openai_responses import OpenAIResponseMessage
|
||||
from llama_stack_api import OpenAIResponseMessage
|
||||
|
||||
return OpenAIResponseMessage(
|
||||
id=input_id,
|
||||
|
|
|
|||
Loading…
Add table
Add a link
Reference in a new issue